commit 6a65d931f7f0745ce8544a45a605ea5c9051947b Author: Ki-Ho Lee Date: Tue Aug 25 17:55:00 2026 +0900 Reproducibility package: UWCA semantic multiple access (TWC submission) diff --git a/README.md b/README.md new file mode 100755 index 0000000..9eefb85 --- /dev/null +++ b/README.md @@ -0,0 +1,60 @@ +# UWCA Semantic Multiple Access — Reproducibility Package + +Simulation code, raw results, and figure sources for the manuscript +"Relevance-Aware Semantic Multiple Access via Meta-Learned User-Wise +Cross-Attention" (submitted to IEEE Transactions on Wireless Communications). + +## Requirements + +- Python 3.10+ with `torch` (CUDA optional; results generated on an NVIDIA + RTX A4500, PyTorch 2.10, CUDA 12.8), `numpy`, `matplotlib`, and + `scikit-learn` (real-data study only). +- All experiments use fixed seed 42 (auxiliary generators seeded as noted in + each script). + +## Layout + +- `rev2/lib.py` — shared library: single-superimposed-signal channel + (Rayleigh / Rician / Nakagami fading, complex phase residuals, timing + offsets, CSI error), UWCA decoder (active-set masking, top-k masking, + I/Q input), closed-form LMMSE receivers, training loops (multi-task + meta-training and first-order MAML), evaluation metrics. +- `rev2/e1_fair_baselines.py` … `rev2/e9_topk_online.py` — one script per + experiment (see the table below). `*_v2/_v3` scripts supersede their + earlier versions where present. +- `rev2/data/` — raw JSON results behind every quoted number. +- `rev2/plot_rev2.py` — regenerates every new figure from `rev2/data/` + without rerunning experiments. +- `legacy/` — scripts for the pre-revision figures (synthetic three-scenario + study, attention maps, beta sweep, real-data study). +- `fig/` — figure PDFs as included in the manuscript. + +## Figure/number → script → data map + +| Manuscript item | Script | Data | +|---|---|---| +| Fig. SER vs SNR (3 scenarios) | `legacy/semantic_correlation_sim.py`, `legacy/maml_semantic.py` | `legacy/results/` | +| Fig. fairness vs optimal linear receivers | `rev2/e1_fair_baselines.py` | `rev2/data/e1_fair_baselines.json` | +| Fig. residual orthogonality | `rev2/e6_residual_orth.py` | `rev2/data/e6_residual_orth.json` | +| Phase-error robustness (complex model, CSI error) | `rev2/e2_phase_iui.py` | `rev2/data/e2_phase_iui.json` | +| Timing-offset robustness | `rev2/e4_v3_async.py` | `rev2/data/e4_v3_async.json` | +| Dynamic user population | `rev2/e3_dynamic_users.py` | `rev2/data/e3_dynamic_users.json` | +| Nonlinear view-network study | `rev2/e5_nonlinear.py` | `rev2/data/e5_nonlinear.json` | +| Meta-adaptation beyond SNR (OOD) | `rev2/e7_v2_meta.py` | `rev2/data/e7_v2_meta.json` | +| End-to-end anti-collapse study | `rev2/e8_v2_e2e.py` | `rev2/data/e8_v2_e2e.json` | +| Online top-k acquisition and timing | `rev2/e9_topk_online.py` | `rev2/data/e9_topk_online.json` | +| Real-data validation | `legacy/revision_realdata_train.py`, `legacy/revision_realdata_plot.py` | `legacy/results/` | + +## Running + +```bash +python rev2/e1_fair_baselines.py # writes rev2/data/e1_fair_baselines.json +python rev2/plot_rev2.py # regenerates the new figure PDFs +``` + +Each experiment script is self-contained and writes its JSON into +`rev2/data/`. + +## Citation and license + +To be completed upon acceptance. diff --git a/fig/fig0_overview2.pdf b/fig/fig0_overview2.pdf new file mode 100755 index 0000000..efc15e9 Binary files /dev/null and b/fig/fig0_overview2.pdf differ diff --git a/fig/fig4_beta_sweep.pdf b/fig/fig4_beta_sweep.pdf new file mode 100755 index 0000000..f458246 Binary files /dev/null and b/fig/fig4_beta_sweep.pdf differ diff --git a/fig/fig6_hlm.pdf b/fig/fig6_hlm.pdf new file mode 100755 index 0000000..2f1dc77 Binary files /dev/null and b/fig/fig6_hlm.pdf differ diff --git a/fig/fig_async.pdf b/fig/fig_async.pdf new file mode 100755 index 0000000..93fd08c Binary files /dev/null and b/fig/fig_async.pdf differ diff --git a/fig/fig_dynusers.pdf b/fig/fig_dynusers.pdf new file mode 100755 index 0000000..1e53202 Binary files /dev/null and b/fig/fig_dynusers.pdf differ diff --git a/fig/fig_fair.pdf b/fig/fig_fair.pdf new file mode 100755 index 0000000..908d614 Binary files /dev/null and b/fig/fig_fair.pdf differ diff --git a/fig/fig_phase2.pdf b/fig/fig_phase2.pdf new file mode 100755 index 0000000..3199379 Binary files /dev/null and b/fig/fig_phase2.pdf differ diff --git a/fig/fig_realdata_c.pdf b/fig/fig_realdata_c.pdf new file mode 100755 index 0000000..f7e25df Binary files /dev/null and b/fig/fig_realdata_c.pdf differ diff --git a/fig/fig_resorth.pdf b/fig/fig_resorth.pdf new file mode 100755 index 0000000..07c6a2f Binary files /dev/null and b/fig/fig_resorth.pdf differ diff --git a/fig/fig_ser_all.pdf b/fig/fig_ser_all.pdf new file mode 100755 index 0000000..970f85d Binary files /dev/null and b/fig/fig_ser_all.pdf differ diff --git a/legacy/maml_semantic.py b/legacy/maml_semantic.py new file mode 100755 index 0000000..9559e1b --- /dev/null +++ b/legacy/maml_semantic.py @@ -0,0 +1,929 @@ +""" +============================================================================= +Multi-User Semantic Communication — PyTorch MAML Training + Evaluation +IEEE JSAC: Meta-Learned Cross-Attention for Multi-User Semantic + Communication over Wireless Fading Channels + +구조 +---- + SemanticEncoder : MLP x_u → e_u ∈ ℝ^d + UserWiseCrossAttn : user-wise cross-attention decoder Y → ê_u (논문 식 4–6) + MAMLTrainer : MAML outer/inner loop over SNR tasks (논문 식 7–9) + +비교 대상 +---------- + OFDMA : 대역폭 B/U 분할 (SNR 패널티 −10log10(U)) + NOMA-SIC : 전력 중첩 + 순차 간섭 제거 + Joint : 고정 SNR 분포에서 표준 joint training + +지표 +---- + SER : cosine-sim(ê_u, e_u) < τ 인 사용자 비율 + ρ : 디코딩된 임베딩의 사용자 간 Pearson 상관계수 + +실행 방법 +---------- + python maml_semantic.py # 학습 + 평가 + python maml_semantic.py --fast # 빠른 디버그 (epoch 축소) + python maml_semantic.py --eval_only --ckpt results/models.pt +============================================================================= +""" + +import argparse +import copy +import warnings +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +from matplotlib.colors import LinearSegmentedColormap + +warnings.filterwarnings("ignore") + +# ───────────────────────────────────────────────────────────────────────────── +# 0. CONFIG +# ───────────────────────────────────────────────────────────────────────────── +def get_cfg(): + p = argparse.ArgumentParser() + p.add_argument("--d", type=int, default=64) + p.add_argument("--U", type=int, default=4) + p.add_argument("--H", type=int, default=4, help="attention heads") + p.add_argument("--tau", type=float, default=0.45) + p.add_argument("--lam", type=float, default=0.1, help="ortho loss weight λ") + p.add_argument("--snr_min", type=float, default=0.0) + p.add_argument("--snr_max", type=float, default=20.0) + p.add_argument("--snr_step", type=float, default=2.0) + p.add_argument("--inner_lr", type=float, default=0.01) + p.add_argument("--inner_steps", type=int, default=5) + p.add_argument("--outer_lr", type=float, default=1e-3) + p.add_argument("--meta_epochs", type=int, default=300) + p.add_argument("--joint_epochs",type=int, default=300) + p.add_argument("--batch", type=int, default=64) + p.add_argument("--n_mc", type=int, default=500) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--outdir", type=str, default="results") + p.add_argument("--eval_only", action="store_true") + p.add_argument("--ckpt", type=str, default=None) + p.add_argument("--fast", action="store_true", + help="빠른 디버그: epoch을 1/10로 축소") + p.add_argument("--device", type=str, default="auto") + p.add_argument("--scenario", type=str, default="DEFAULT", + help="훈련 시나리오: DEFAULT/HIGH/LOW/MIX/HETERO/ASYM/ALL") + p.add_argument("--decoder_only", action="store_true", + help="Option A: IdentityEncoder(고정) + decoder만 MAML 훈련") + args = p.parse_args() + if args.fast: + args.meta_epochs = max(30, args.meta_epochs // 10) + args.joint_epochs = max(30, args.joint_epochs // 10) + args.n_mc = max(50, args.n_mc // 10) + return args + + +# ───────────────────────────────────────────────────────────────────────────── +# 1. DATA GENERATION (자율주행 합성 임베딩) +# ───────────────────────────────────────────────────────────────────────────── +# u0: 보행자 bounding-box (scene 결합도 높음) +# u1: 신호등 상태 (중간) +# u2: 차선 세그먼테이션 (낮음) +# u3: 차량 속도/방향 (가장 낮음) +BLEND = [0.55, 0.45, 0.30, 0.20] +USER_LABELS = ['보행자\n(U1)', '신호등\n(U2)', '차선\n(U3)', '속도\n(U4)'] +USER_COLORS = ['#1565C0', '#2E7D32', '#C62828', '#6A1B9A'] + +# Scenario configs matching semantic_correlation_sim.py +SCENARIO_CONFIGS = { + 'DEFAULT': {'beta_u': [0.55, 0.45, 0.30, 0.20], 'scenes': [0, 0, 0, 0]}, + 'HIGH': {'beta_u': [0.65, 0.65, 0.60, 0.60], 'scenes': [0, 0, 0, 0]}, + 'LOW': {'beta_u': [0.65, 0.05, 0.05, 0.05], 'scenes': [0, 1, 2, 3]}, + 'MIX': {'beta_u': [0.65, 0.65, 0.05, 0.05], 'scenes': [0, 0, 1, 2]}, + 'HETERO': {'beta_u': [0.75, 0.75, 0.45, 0.08], 'scenes': [0, 0, 0, 1]}, + 'ASYM': {'beta_u': [0.72, 0.58, 0.35, 0.12], 'scenes': [0, 0, 0, 0]}, +} + + +def gen_embeddings(n: int, d: int, U: int, rng, + scenario_cfg=None) -> torch.Tensor: + """단위 정규화된 ground-truth 임베딩 (n, U, d) + scenario_cfg: dict with 'beta_u' and 'scenes' keys (from SCENARIO_CONFIGS). + If None, uses the default BLEND with a single shared scene. + """ + if scenario_cfg is None: + blend = BLEND + scenes = [0] * U + else: + blend = scenario_cfg['beta_u'] + scenes = scenario_cfg['scenes'] + + # One unit-norm scene vector per unique scene key (full D dims, matches sim) + unique_scenes = sorted(set(scenes)) + scene_vecs: dict = {} + for sc in unique_scenes: + v = rng.standard_normal(d) + scene_vecs[sc] = v / (np.linalg.norm(v) + 1e-8) + + embs = [] + for u in range(U): + b = blend[u] if u < len(blend) else 0.15 + private = rng.standard_normal((n, d)) + p_hat = private / (np.linalg.norm(private, axis=-1, keepdims=True) + 1e-8) + s = scene_vecs[scenes[u]] + # Eq. (2): e_u = sqrt(1-β²)·p̂_u + β·s (semantic_correlation_sim.py convention) + e = np.sqrt(max(1 - b ** 2, 0)) * p_hat + b * s[None, :] + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + embs.append(e) + return torch.from_numpy(np.stack(embs, axis=1)).float() # (n, U, d) + + +# ───────────────────────────────────────────────────────────────────────────── +# 2. CHANNEL MODELS (SE — Shared Embedding superposition framework) +# ───────────────────────────────────────────────────────────────────────────── +def _block_masks(U: int, d: int, device) -> torch.Tensor: + """Hard block masks (U, d): user u owns dims [u·DPU, (u+1)·DPU). + Matches semantic_correlation_sim.py MASKS construction exactly.""" + DPU = d // U + masks = torch.zeros(U, d, device=device) + for u in range(U): + masks[u, u * DPU:(u + 1) * DPU] = 1.0 + return masks + + +def se_channel(E: torch.Tensor, snr_db: float) -> torch.Tensor: + """SE superposition channel (논문 Eq. 3). + + x_u = e_u ⊙ m_u (hard block mask; user u owns D/U contiguous dims) + y_tx = Σ_u x_u (superimposed D-dim signal) + y_rx,u = h_u · y_tx + n_u (independent Rayleigh per user) + + E : (n, U, d) unit-norm embeddings + Returns Y_rx : (n, U, d) — row u is user u's received copy of y_tx. + + Cross-attention 구조 설명: + R_{u,i} = y_rx,u ⊙ m_i 는 user u의 수신 신호에서 subspace i를 추출. + h_u · x_i[dims_i] + noise 만 남으므로, beta_ui > 0이면 e_u 정보를 담고 있음. + """ + n, U, d = E.shape + masks = _block_masks(U, d, E.device) # (U, d) + X = E * masks[None, :, :] # (n, U, d) + Ytx = X.sum(1) # (n, d) superimposed + + h = (torch.randn(n, U, 1, device=E.device) ** 2 + + torch.randn(n, U, 1, device=E.device) ** 2).sqrt() * (0.5 ** 0.5) # (n, U, 1) + + snr_lin = 10 ** (snr_db / 10) + noise_std = (Ytx.pow(2).mean() / snr_lin).sqrt() + Yrx = h * Ytx[:, None, :] + torch.randn(n, U, d, device=E.device) * noise_std + return Yrx # (n, U, d) + + +def se_ofdma_decode(Y_rx: torch.Tensor) -> torch.Tensor: + """SE-OFDMA decoder: ê_u = normalize(y_rx,u ⊙ m_u). + Each user uses only their own D/U-dim subspace block; no SNR penalty. + Matches ofdma_se_decoder() in semantic_correlation_sim.py.""" + n, U, d = Y_rx.shape + masks = _block_masks(U, d, Y_rx.device) # (U, d) + return F.normalize(Y_rx * masks[None, :, :], dim=-1) # (n, U, d) + + +def se_noma_decode(E: torch.Tensor, snr_db: float) -> torch.Tensor: + """Full-band power-domain NOMA with SIC (standard NOMA, fair comparison). + + All U users share the SAME full d-dim band — no subspace masking — so the + received signal is a single d-dim power-domain superposition: + x_u = sqrt(p_u) · e_u (full-band, power-weighted) + y = Σ_u h_u · sqrt(p_u) · e_u + n (single d-dim signal at BS) + + This reflects NOMA's defining resource advantage fairly: every user accesses + the full d-dim band (vs. OFDMA's exclusive d/U-dim block) at the cost of + inter-user interference, while the total transmit power Σ_u p_u = 1 matches + the OFDMA budget, keeping the comparison both power- and bandwidth-fair. + Each user is reconstructed from the full d-dim signal via successive + interference cancellation in descending allocated-power order. + + Matches noma_ul_channel()/noma_sic_decoder() in semantic_correlation_sim.py + and noma_ch()/noma_sic() in revision_realdata_plot.py. + + E : (n, U, d) unit-norm ground-truth embeddings. + Returns decoded : (n, U, d) per-user estimates over the full d-dim band. + """ + n, U, d = E.shape + pa = torch.tensor([0.40, 0.30, 0.20, 0.10], device=E.device)[:U] + pa = pa / pa.sum() # Σ p_u = 1 (power-fair) + + h = (torch.randn(n, U, 1, device=E.device) ** 2 + + torch.randn(n, U, 1, device=E.device) ** 2).sqrt() * (0.5 ** 0.5) # (n, U, 1) + + y = (E * pa.sqrt()[None, :, None] * h).sum(1) # (n, d) full-band superposition + snr_lin = 10 ** (snr_db / 10) + noise_std = (y.pow(2).mean() / snr_lin).sqrt() + y = y + torch.randn(n, d, device=E.device) * noise_std # (n, d) + + order = torch.argsort(pa, descending=True) # high-power first + res = y.clone() # (n, d) shared residual + decoded = torch.zeros(n, U, d, device=E.device) # (n, U, d) + for u_idx in order: + u = int(u_idx.item()) + ê_u = F.normalize(res / (h[:, u, :] + 1e-8), dim=-1) # (n, d) over full band + decoded[:, u, :] = ê_u + res = res - h[:, u, :] * pa[u].sqrt() * ê_u # full-band cancellation + + return decoded # (n, U, d) + + +def se_sfdma_decode(E: torch.Tensor, snr_db: float) -> torch.Tensor: + """SFDMA — Semantic Feature Division Multiple Access (Ma et al., 2024). + + Each user maps its embedding onto an assigned orthonormal d/U-dim *semantic* + subspace; all users transmit simultaneously over the SAME full d-dim band, and + the receiver separates them by projecting onto each user's subspace: + x_u = P_u e_u (P_u: projector onto user u's semantic subspace) + y = Σ_u h_u · x_u + n (full-band superposition) + ê_u = normalize(P_u y / h_u) + + Fair vs. NOMA/UWCA: SFDMA shares the full band (not OFDMA's exclusive 1/U + physical sub-band) and is given the best case of *perfectly* orthogonal + subspaces, so the projections separate users with no inter-user interference. + The subspaces are taken as the canonical orthogonal partition (the block + basis) so the comparison isolates the multiple-access mechanism rather than an + arbitrary basis–data alignment. Because the orthogonality confines each user + to d/U effective dimensions — the same subspace ceiling as OFDMA — and discards + the inter-user correlation, SFDMA coincides with OFDMA despite using the full + band. Matches sfdma() in revision_realdata_plot.py. + + E : (n, U, d) unit-norm embeddings. Returns (n, U, d) estimates. + """ + n, U, d = E.shape + masks = _block_masks(U, d, E.device) # (U, d) orthogonal subspaces + + h = (torch.randn(n, U, 1, device=E.device) ** 2 + + torch.randn(n, U, 1, device=E.device) ** 2).sqrt() * (0.5 ** 0.5) # (n, U, 1) + + X = E * masks[None, :, :] # (n, U, d) project onto own subspace + y = (h * X).sum(1) # (n, d) full-band superposition + snr_lin = 10 ** (snr_db / 10) + noise_std = (y.pow(2).mean() / snr_lin).sqrt() + y = y + torch.randn(n, d, device=E.device) * noise_std # (n, d) + + return torch.stack( + [F.normalize((y * masks[u]) / (h[:, u, :] + 1e-8), dim=-1) for u in range(U)], 1) + + +# ───────────────────────────────────────────────────────────────────────────── +# 3. NEURAL MODULES +# ───────────────────────────────────────────────────────────────────────────── +class SemanticEncoder(nn.Module): + """f_φ : ℝ^d → ℝ^d (MLP + LayerNorm + 단위 정규화)""" + def __init__(self, d: int): + super().__init__() + self.net = nn.Sequential( + nn.Linear(d, d * 2), nn.LayerNorm(d * 2), nn.GELU(), + nn.Linear(d * 2, d), + ) + + def forward(self, x): + return F.normalize(self.net(x), dim=-1) + + +class IdentityEncoder(nn.Module): + """No-op encoder: L2-normalizes input only (no learnable params). + Used for decoder-only training (Option A) so that the channel model + directly transmits the raw embeddings, matching the analytical simulation.""" + def forward(self, x): + return F.normalize(x, dim=-1) + + +class UserWiseCrossAttention(nn.Module): + """ + User-wise cross-attention decoder (논문 Section III-A, 식 4–6) + + 입력 Y : (n, U, d) 수신 임베딩 (잡음 포함) + 출력 Ê : (n, U, d) 정제된 임베딩 + 복잡도 O(U d²) + """ + def __init__(self, d: int, U: int, H: int = 4): + super().__init__() + assert d % H == 0 + self.d = d; self.U = U; self.H = H; self.dk = d // H + # JSAC convention: learnable per-user query vectors {q_u}, not signal-derived + self.q_vectors = nn.Parameter(torch.randn(U, d) * (d ** -0.5)) # (U, d) + self.eta = nn.Parameter(torch.ones(1)) # sharpness η + self.W_K = nn.Linear(d, d, bias=False) + self.W_V = nn.Linear(d, d, bias=False) + self.W_O = nn.Linear(d, d, bias=False) + self.norm = nn.LayerNorm(d) + # 학습 가능한 soft mask m_i ∈ [0,1]^d (식 4) + # 초기화: hard block mask logit (+3 = sigmoid → 0.95, -3 → 0.05) + # 이 초기화가 없으면 모든 mask = 0.5 → K feature가 모든 i에서 동일 + # → softmax가 항상 uniform(1/U) → 학습 무력화 + init_logits = torch.full((U, d), -3.0) + DPU = d // U + for u_ in range(U): + init_logits[u_, u_ * DPU:(u_ + 1) * DPU] = 3.0 # own block 강조 + self.mask_logits = nn.Parameter(init_logits) + + def forward(self, Y): + """Y : (n, U, d) → (Ê, alpha)""" + n, U, d = Y.shape + masks = torch.sigmoid(self.mask_logits) # (U, d) + + # R_{u,i} = y_u ⊙ m_i → (n, U_q, U_k, d) + R = Y.unsqueeze(2) * masks[None, None, :, :] # broadcast + + # JSAC: q_u is a fixed learnable vector per user, independent of received signal + Q = self.q_vectors.unsqueeze(0).expand(n, -1, -1) # (n, U, d) + K = self.W_K(R) # (n, U, U, d) + V = self.W_V(R) + + # multi-head reshape + def mh(t): + return t.reshape(*t.shape[:-1], self.H, self.dk) + + Q_ = mh(Q) # (n,Uq,H,dk) + K_ = mh(K) # (n,Uq,Uk,H,dk) + V_ = mh(V) + + # score : q_u^T k_i / sqrt(dk) + Q_e = Q_.unsqueeze(3) # (n,Uq,H,1,dk) + K_t = K_.permute(0, 1, 3, 2, 4) # (n,Uq,H,Uk,dk) + scores = self.eta * (Q_e * K_t).sum(-1) / (self.dk ** 0.5) # η * q_u^T k_i / √dk + alpha = F.softmax(scores, dim=-1) # (n,Uq,H,Uk) + + V_t = V_.permute(0, 1, 3, 2, 4) # (n,Uq,H,Uk,dk) + ctx = (alpha.unsqueeze(-1) * V_t).sum(3) # (n,Uq,H,dk) + ctx = self.W_O(ctx.reshape(n, U, d)) # (n,Uq,d) + + own = Y * masks[None, :, :] # 자기 유저 residual + out = F.normalize(self.norm(ctx + own), dim=-1) + return out, alpha.mean(0) # alpha: (Uq,H,Uk) + + +class SemanticCommSystem(nn.Module): + def __init__(self, d, U, H, decoder_only=False): + super().__init__() + self.encoder = IdentityEncoder() if decoder_only else SemanticEncoder(d) + self.decoder = UserWiseCrossAttention(d, U, H) + + def forward(self, X, snr_db): + n, U, d = X.shape + E = self.encoder(X.reshape(n * U, d)).reshape(n, U, d) + Y = se_channel(E, snr_db) # always SE superposition channel + Ehat, alpha = self.decoder(Y) + return E, Ehat, alpha + + +# ───────────────────────────────────────────────────────────────────────────── +# 4. LOSS (논문 식 8) +# ───────────────────────────────────────────────────────────────────────────── +def semantic_loss(Ehat, E, lam=0.1): + """ + L = (1/U)Σ(1 - cos(ê_u,e_u)) + λ Σ_{u≠v}|ρ(ê_u,ê_v)| + """ + cos = (Ehat * E).sum(-1) + distortion = (1 - cos).mean() + + U = Ehat.shape[1] + emb = Ehat.mean(0) # (U, d) + ec = emb - emb.mean(1, keepdim=True) + en = F.normalize(ec, dim=1) + C = en @ en.T + mask = ~torch.eye(U, dtype=torch.bool, device=Ehat.device) + ortho = C[mask].abs().mean() + + return distortion + lam * ortho, distortion.item(), ortho.item() + + +# ───────────────────────────────────────────────────────────────────────────── +# 5. MAML TRAINER (논문 식 7, 9) +# ───────────────────────────────────────────────────────────────────────────── +class MAMLTrainer: + def __init__(self, model, cfg, device, rng, scenario_cfg=None): + self.model = model + self.cfg = cfg + self.device = device + self.rng = rng + self.scenario_cfg = scenario_cfg # None → default BLEND + mask_params = [p for n, p in model.named_parameters() if 'mask_logits' in n] + other_params = [p for n, p in model.named_parameters() if 'mask_logits' not in n] + self.optimizer = torch.optim.Adam([ + {'params': other_params, 'lr': cfg.outer_lr}, + {'params': mask_params, 'lr': cfg.outer_lr * 100}, + ]) + self.snr_tasks = np.arange(cfg.snr_min, cfg.snr_max + 1e-6, cfg.snr_step) + + def _inner_adapt(self, snr): + """Inner-loop: adapted copy of model for task T_k = SNR γ_k""" + adapted = copy.deepcopy(self.model) + opt_in = torch.optim.SGD(adapted.parameters(), lr=self.cfg.inner_lr) + for _ in range(self.cfg.inner_steps): + X = gen_embeddings(self.cfg.batch, self.cfg.d, + self.cfg.U, self.rng, + self.scenario_cfg).to(self.device) + E, Ehat, _ = adapted(X, snr) + loss, _, _ = semantic_loss(Ehat, E, self.cfg.lam) + opt_in.zero_grad(); loss.backward(); opt_in.step() + return adapted + + def meta_step(self): + """Outer-loop: first-order MAML (Reptile-style aggregation)""" + fo_loss = torch.tensor(0.0, device=self.device) + for snr in self.snr_tasks: + X = gen_embeddings(self.cfg.batch, self.cfg.d, + self.cfg.U, self.rng, + self.scenario_cfg).to(self.device) + E, Ehat, _ = self.model(X, float(snr)) + loss, _, _ = semantic_loss(Ehat, E, self.cfg.lam) + fo_loss = fo_loss + loss + fo_loss = fo_loss / len(self.snr_tasks) + self.optimizer.zero_grad() + fo_loss.backward() + nn.utils.clip_grad_norm_(self.model.parameters(), 5.0) + self.optimizer.step() + return fo_loss.item() + + def train(self): + history = [] + print("=" * 60) + print(f"MAML Training ({self.cfg.meta_epochs} epochs, " + f"{len(self.snr_tasks)} SNR tasks)") + print("=" * 60) + for ep in range(1, self.cfg.meta_epochs + 1): + loss = self.meta_step() + history.append(loss) + if ep % max(1, self.cfg.meta_epochs // 6) == 0: + print(f" Epoch {ep:4d}/{self.cfg.meta_epochs} loss={loss:.4f}") + return history + + +# ───────────────────────────────────────────────────────────────────────────── +# 6. JOINT TRAINING BASELINE +# ───────────────────────────────────────────────────────────────────────────── +def train_joint(model, cfg, device, rng, scenario_cfg=None): + mask_params = [p for n, p in model.named_parameters() if 'mask_logits' in n] + other_params = [p for n, p in model.named_parameters() if 'mask_logits' not in n] + opt = torch.optim.Adam([ + {'params': other_params, 'lr': cfg.outer_lr}, + {'params': mask_params, 'lr': cfg.outer_lr * 100}, + ]) + history = [] + print("=" * 60) + print(f"Joint Training ({cfg.joint_epochs} epochs)") + print("=" * 60) + for ep in range(1, cfg.joint_epochs + 1): + snr = float(np.random.uniform(cfg.snr_min, cfg.snr_max)) + X = gen_embeddings(cfg.batch, cfg.d, cfg.U, rng, scenario_cfg).to(device) + E, Ehat, _ = model(X, snr) + loss, _, _ = semantic_loss(Ehat, E, cfg.lam) + opt.zero_grad(); loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), 5.0) + opt.step() + history.append(loss.item()) + if ep % max(1, cfg.joint_epochs // 6) == 0: + print(f" Epoch {ep:4d}/{cfg.joint_epochs} loss={loss.item():.4f}") + return history + + +# ───────────────────────────────────────────────────────────────────────────── +# 7. EVALUATION (semantic_sim.py 구조 그대로 유지) +# ───────────────────────────────────────────────────────────────────────────── +def _normalize_np(E): + return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8) + +def cos_mean(Eh, Egt): + return (Eh * Egt).sum(-1).mean() + +def ser_total(Eh, Egt, tau): + return ((Eh * Egt).sum(-1) < tau).mean() + +def ser_per_user(Eh, Egt, tau): + return ((Eh * Egt).sum(-1) < tau).mean(0) # (U,) + +def corr_matrix(Eh): + e = Eh.mean(0) # (U, D) + ec = e - e.mean(1, keepdims=True) + en = ec / (np.linalg.norm(ec, axis=1, keepdims=True) + 1e-8) + return en @ en.T # (U, U) + + +@torch.no_grad() +def evaluate_model(model, cfg, device, rng, channel="se", scenario_cfg=None): + """ + SNR_DB 전 구간에 걸쳐 SER / cosine / per-user SER 계산. + semantic_sim.py의 시뮬레이션 루프와 동일한 방식. + """ + SNR_DB = np.arange(cfg.snr_min, cfg.snr_max + 1e-6, cfg.snr_step) + model.eval() + res = {"ser": [], "cos": [], "sp": [], "rho": []} + + for snr in SNR_DB: + ser_acc = 0.0; cos_acc = 0.0 + sp_acc = np.zeros(cfg.U); rho_acc = [] + + for _ in range(cfg.n_mc): + Egt = gen_embeddings(cfg.batch, cfg.d, cfg.U, rng, scenario_cfg) + X = Egt.to(device) + + # 채널 통과 — 모든 방식이 SE 수퍼포지션 채널 공유 + if channel == "ofdma": + # SE-OFDMA: 같은 SE 채널, own D/U block만 사용 + E = F.normalize(X.reshape(-1, cfg.d), dim=-1).reshape(-1, cfg.U, cfg.d) + Y = se_channel(E, float(snr)) + Ehat = se_ofdma_decode(Y) + elif channel == "noma": + # SE-NOMA-SIC: full-band power-domain superposition (own channel + SIC) + E = F.normalize(X.reshape(-1, cfg.d), dim=-1).reshape(-1, cfg.U, cfg.d) + Ehat = se_noma_decode(E, float(snr)) + elif channel == "sfdma": + # SFDMA: full-band, orthogonal semantic-subspace division (Ma 2024) + E = F.normalize(X.reshape(-1, cfg.d), dim=-1).reshape(-1, cfg.U, cfg.d) + Ehat = se_sfdma_decode(E, float(snr)) + else: + # MAML+Attn / Joint+Attn: SE 채널 + cross-attention decoder + E, Ehat, _ = model(X, float(snr)) + + Eh = Ehat.cpu().numpy() + Egt_np = E.cpu().numpy() + + ser_acc += ser_total(Eh, Egt_np, cfg.tau) + cos_acc += cos_mean(Eh, Egt_np) + sp_acc += ser_per_user(Eh, Egt_np, cfg.tau) + rho_acc.append(corr_matrix(Eh)) + + res["ser"].append(ser_acc / cfg.n_mc) + res["cos"].append(cos_acc / cfg.n_mc) + res["sp"].append(sp_acc / cfg.n_mc) + res["rho"].append(np.mean(rho_acc, axis=0)) + + for k in res: + res[k] = np.array(res[k]) + return res + + +@torch.no_grad() +def get_attention_map(model, snr, cfg, device, rng): + model.eval() + attn_sum = None + for _ in range(cfg.n_mc): + X = gen_embeddings(cfg.batch, cfg.d, cfg.U, rng).to(device) + E = model.encoder(X.reshape(-1, cfg.d)).reshape(-1, cfg.U, cfg.d) + Y = se_channel(E, float(snr)) + _, alpha = model.decoder(Y) # (Uq, H, Uk) + a = alpha.mean(1).cpu().numpy() # (Uq, Uk) — avg over heads + attn_sum = a if attn_sum is None else attn_sum + a + return attn_sum / cfg.n_mc + + +# ───────────────────────────────────────────────────────────────────────────── +# 8. 9-PANEL FIGURE (semantic_sim.py 그대로 재현) +# ───────────────────────────────────────────────────────────────────────────── +def plot_results(SNR_DB, res_maml, res_joint, res_ofdma, res_noma, + rho_j, rho_m, attn_m_mc, out_path, cfg): + + U = cfg.UOF + KP = 'MAML+Attn\n(제안)' + MCFG = { + 'OFDMA': ('#546E7A', 's--', 1.6, 'OFDMA'), + 'NOMA-SIC': ('#E65100', '^-.', 1.6, 'NOMA-SIC'), + 'Joint+Attn': ('#C62828', 'D--', 1.8, 'Joint+Attn'), + KP: ('#1565C0', 'o-', 2.5, 'MAML+Attn (제안)'), + } + res_all = {'OFDMA': res_ofdma, 'NOMA-SIC': res_noma, + 'Joint+Attn': res_joint, KP: res_maml} + + cmap_r = LinearSegmentedColormap.from_list( + 'r', ['#1565C0', '#FFFFFF', '#C62828'], N=256) + cmap_a = LinearSegmentedColormap.from_list( + 'a', ['#F5F5F5', '#1565C0'], N=256) + + fig = plt.figure(figsize=(18, 15)) + fig.patch.set_facecolor('#F8F9FA') + gs = gridspec.GridSpec(3, 3, figure=fig, hspace=0.48, wspace=0.38, + left=0.07, right=0.97, top=0.93, bottom=0.06) + + idx10 = int((10 - cfg.snr_min) / cfg.snr_step) # SNR=10dB 인덱스 + + # (a) SER vs SNR + ax = fig.add_subplot(gs[0, 0]); ax.set_facecolor('white') + for k, (c, mk, lw, lb) in MCFG.items(): + ax.semilogy(SNR_DB, res_all[k]['ser'], mk, lw=lw, ms=6, color=c, label=lb) + d10 = res_ofdma['ser'][idx10] - res_maml['ser'][idx10] + ax.annotate(f'Δ={d10:.3f}\n@ 10 dB', + xy=(10, res_maml['ser'][idx10]), + xytext=(13, res_maml['ser'][idx10] * 4), + fontsize=8.5, color='#1565C0', + arrowprops=dict(arrowstyle='->', color='#1565C0', lw=1.2)) + ax.set_xlabel('SNR (dB)', fontsize=11); ax.set_ylabel('SER', fontsize=11) + ax.set_title('(a) SER vs SNR', fontsize=12, fontweight='bold') + ax.legend(fontsize=9); ax.grid(True, alpha=0.35); ax.set_xlim(0, 20) + + # (b) 코사인 유사도 + ax = fig.add_subplot(gs[0, 1]); ax.set_facecolor('white') + for k, (c, mk, lw, lb) in MCFG.items(): + ax.plot(SNR_DB, res_all[k]['cos'], mk, lw=lw, ms=6, color=c, label=lb) + ax.axhline(cfg.tau, color='gray', lw=1.2, ls=':', label=f'τ={cfg.tau}') + ax.set_xlabel('SNR (dB)', fontsize=11); ax.set_ylabel('코사인 유사도', fontsize=11) + ax.set_title('(b) 코사인 유사도 vs SNR', fontsize=12, fontweight='bold') + ax.legend(fontsize=9); ax.grid(True, alpha=0.35) + ax.set_xlim(0, 20); ax.set_ylim(0.35, 1.02) + + # (c) SER 개선량 + ax = fig.add_subplot(gs[0, 2]); ax.set_facecolor('white') + comps = [('vs OFDMA', 'OFDMA', '#546E7A'), + ('vs NOMA-SIC', 'NOMA-SIC', '#E65100'), + ('vs Joint+Attn', 'Joint+Attn', '#C62828')] + offs = [-0.3, 0.0, 0.3] + for (lb, base, col), off in zip(comps, offs): + ax.bar(SNR_DB + off, res_all[base]['ser'] - res_maml['ser'], + width=0.28, alpha=0.80, color=col, label=lb) + ax.axhline(0, color='black', lw=0.8) + ax.set_xlabel('SNR (dB)', fontsize=11); ax.set_ylabel('SER 개선량', fontsize=11) + ax.set_title('(c) SER 개선량 (베이스라인 − 제안)', fontsize=12, fontweight='bold') + ax.legend(fontsize=9); ax.grid(True, alpha=0.25, axis='y') + + # (d) 제안 사용자별 SER + ax = fig.add_subplot(gs[1, 0]); ax.set_facecolor('white') + for ui in range(U): + ax.semilogy(SNR_DB, res_maml['sp'][:, ui], 'o-', lw=1.8, ms=5, + color=USER_COLORS[ui], label=USER_LABELS[ui]) + ax.set_xlabel('SNR (dB)', fontsize=11); ax.set_ylabel('SER', fontsize=11) + ax.set_title('(d) 제안 — 사용자별 SER', fontsize=12, fontweight='bold') + ax.legend(fontsize=8); ax.grid(True, alpha=0.35); ax.set_xlim(0, 20) + + # (e) OFDMA 사용자별 SER + ax = fig.add_subplot(gs[1, 1]); ax.set_facecolor('white') + for ui in range(U): + ax.semilogy(SNR_DB, res_ofdma['sp'][:, ui], 's--', lw=1.6, ms=5, + color=USER_COLORS[ui], label=USER_LABELS[ui]) + ax.set_xlabel('SNR (dB)', fontsize=11); ax.set_ylabel('SER', fontsize=11) + ax.set_title('(e) OFDMA — 사용자별 SER', fontsize=12, fontweight='bold') + ax.legend(fontsize=8); ax.grid(True, alpha=0.35); ax.set_xlim(0, 20) + + # (f) SER @ 10 dB 막대 + ax = fig.add_subplot(gs[1, 2]); ax.set_facecolor('white') + ms = ['OFDMA', 'NOMA-SIC', 'Joint+Attn', KP] + s10 = [res_all[m]['ser'][idx10] for m in ms] + lb10 = ['OFDMA', 'NOMA-SIC', 'Joint\n+Attn', 'MAML+Attn\n(제안)'] + c10 = ['#546E7A', '#E65100', '#C62828', '#1565C0'] + bars = ax.bar(range(4), s10, color=c10, width=0.55, + edgecolor='white', linewidth=1.2) + ax.set_xticks(range(4)); ax.set_xticklabels(lb10, fontsize=9.5) + ax.set_ylabel('SER @ 10 dB', fontsize=11) + ax.set_title('(f) 방법별 SER @ 10 dB', fontsize=12, fontweight='bold') + ax.grid(True, alpha=0.3, axis='y') + for b, v, c in zip(bars, s10, c10): + ax.text(b.get_x() + b.get_width() / 2, v + 0.003, f'{v:.3f}', + ha='center', va='bottom', fontsize=10, fontweight='bold', color=c) + + # (g) 상관계수 — Joint + ax = fig.add_subplot(gs[2, 0]); ax.set_facecolor('white') + im = ax.imshow(rho_j, cmap=cmap_r, vmin=-0.3, vmax=0.3, aspect='auto') + ax.set_xticks(range(U)); ax.set_yticks(range(U)) + ax.set_xticklabels([f'U{i+1}' for i in range(U)], fontsize=10) + ax.set_yticklabels([f'U{i+1}' for i in range(U)], fontsize=10) + for i in range(U): + for j in range(U): + v = rho_j[i, j] + ax.text(j, i, f'{v:.3f}', ha='center', va='center', fontsize=11, + fontweight='bold', color='white' if abs(v) > 0.15 else 'black') + plt.colorbar(im, ax=ax, fraction=0.046) + ax.set_title('(g) 상관계수 — Joint Training', fontsize=12, fontweight='bold') + ax.set_xlabel('사용자 j', fontsize=10); ax.set_ylabel('사용자 i', fontsize=10) + + # (h) 상관계수 — MAML + ax = fig.add_subplot(gs[2, 1]); ax.set_facecolor('white') + im = ax.imshow(rho_m, cmap=cmap_r, vmin=-0.3, vmax=0.3, aspect='auto') + ax.set_xticks(range(U)); ax.set_yticks(range(U)) + ax.set_xticklabels([f'U{i+1}' for i in range(U)], fontsize=10) + ax.set_yticklabels([f'U{i+1}' for i in range(U)], fontsize=10) + for i in range(U): + for j in range(U): + v = rho_m[i, j] + ax.text(j, i, f'{v:.3f}', ha='center', va='center', fontsize=11, + fontweight='bold', color='white' if abs(v) > 0.15 else 'black') + plt.colorbar(im, ax=ax, fraction=0.046) + ax.set_title('(h) 상관계수 — MAML (제안)', fontsize=12, fontweight='bold') + ax.set_xlabel('사용자 j', fontsize=10); ax.set_ylabel('사용자 i', fontsize=10) + + # (i) Attention heatmap + ax = fig.add_subplot(gs[2, 2]); ax.set_facecolor('white') + im = ax.imshow(attn_m_mc, cmap=cmap_a, + vmin=0, vmax=attn_m_mc.max(), aspect='auto') + sh = ['보행자\n(U1)', '신호등\n(U2)', '차선\n(U3)', '속도\n(U4)'] + ax.set_xticks(range(U)); ax.set_yticks(range(U)) + ax.set_xticklabels(sh[:U], fontsize=9); ax.set_yticklabels(sh[:U], fontsize=9) + for i in range(U): + for j in range(U): + v = attn_m_mc[i, j] + ax.text(j, i, f'{v:.3f}', ha='center', va='center', fontsize=11, + fontweight='bold', + color='white' if v > attn_m_mc.max() * 0.5 else '#0D1B3E') + plt.colorbar(im, ax=ax, fraction=0.046) + ax.set_title(f'(i) 어텐션 가중치 α_{{u,i}} — MAML @ 10dB', + fontsize=12, fontweight='bold') + ax.set_xlabel('참조 사용자 i', fontsize=10) + ax.set_ylabel('질의 사용자 u', fontsize=10) + + fig.suptitle( + 'Multi-User Semantic Communication: User-Wise Attention vs Orthogonal Allocation\n' + f'(자율주행 시나리오 — U={U}, d={cfg.d}, Rayleigh Fading)', + fontsize=13, fontweight='bold', y=0.97) + + plt.savefig(out_path, dpi=150, bbox_inches='tight', facecolor='#F8F9FA') + plt.close() + print(f" 그림 저장 → {out_path}") + + +# ───────────────────────────────────────────────────────────────────────────── +# 9. SUMMARY PRINT (semantic_sim.py 스타일) +# ───────────────────────────────────────────────────────────────────────────── +def print_summary(res_maml, res_joint, res_ofdma, res_noma, + rho_j, rho_m, attn_m, cfg): + SNR_DB = np.arange(cfg.snr_min, cfg.snr_max + 1e-6, cfg.snr_step) + idx = {4: int((4 - cfg.snr_min) / cfg.snr_step), + 10: int((10 - cfg.snr_min) / cfg.snr_step), + 16: int((16 - cfg.snr_min) / cfg.snr_step)} + U = cfg.U + mask = ~np.eye(U, dtype=bool) + + print("\n" + "=" * 62) + print("NUMERICAL SUMMARY") + print("=" * 62) + print(f"{'Method':<22}{'SER@4dB':>9}{'SER@10dB':>10}" + f"{'SER@16dB':>10}{'Cos@10dB':>10}") + print("-" * 62) + for lb, res in [('OFDMA', res_ofdma), ('NOMA-SIC', res_noma), + ('Joint+Attn', res_joint), ('MAML+Attn (제안)', res_maml)]: + print(f"{lb:<22}{res['ser'][idx[4]]:>9.4f}" + f"{res['ser'][idx[10]]:>10.4f}" + f"{res['ser'][idx[16]]:>10.4f}" + f"{res['cos'][idx[10]]:>10.4f}") + + print(f"\n임베딩 상관계수 |ρ| (off-diag @ 10 dB):") + print(f" Joint : mean={np.abs(rho_j[mask]).mean():.4f} " + f"[{rho_j[mask].min():.4f}, {rho_j[mask].max():.4f}]") + print(f" MAML : mean={np.abs(rho_m[mask]).mean():.4f} " + f"[{rho_m[mask].min():.4f}, {rho_m[mask].max():.4f}]") + + print(f"\n어텐션 가중치 α (MAML @ 10 dB):") + hdr = ''.join([f" U{j+1}" for j in range(U)]) + print(f"{'':>14}{hdr}") + names = ['보행자', '신호등', '차선 ', '속도 '] + for i in range(U): + row = ''.join([f" {attn_m[i,j]:>7.4f}" for j in range(U)]) + nm = names[i] if i < len(names) else f'U{i+1} ' + print(f" U{i+1}({nm}){row}") + + print(f"\n핵심: α[보행자→신호등]={attn_m[0,1]:.4f} (높음) vs " + f"α[보행자→속도]={attn_m[0,3]:.4f} (낮음)") + print(f" SER 개선 vs OFDMA @ 10dB: " + f"{res_ofdma['ser'][idx[10]] - res_maml['ser'][idx[10]]:.4f}") + print("=" * 62) + + +# ───────────────────────────────────────────────────────────────────────────── +# 10. JSON EXPORT (for overlay in semantic_correlation_sim.py) +# ───────────────────────────────────────────────────────────────────────────── +import json + +def export_results_json(scenario: str, SNR_DB, res_maml, res_joint, + res_ofdma, res_noma, outdir: Path): + """Export SER results to JSON so semantic_correlation_sim.py can overlay them.""" + data = { + "scenario": scenario, + "decoder_only": True, + "snr_db": SNR_DB.tolist(), + "maml_ser": res_maml["ser"].tolist(), + "joint_ser": res_joint["ser"].tolist(), + "ofdma_ser": res_ofdma["ser"].tolist(), + "noma_ser": res_noma["ser"].tolist(), + } + path = outdir / f"trained_{scenario}.json" + with open(path, "w") as f: + json.dump(data, f, indent=2) + print(f"결과 JSON 저장 → {path}") + + +# ───────────────────────────────────────────────────────────────────────────── +# 11. MAIN +# ───────────────────────────────────────────────────────────────────────────── +def _run_one_scenario(scenario: str, cfg, device, np_rng, outdir: Path): + """Train + evaluate one scenario; save checkpoint + JSON.""" + scenario_cfg = SCENARIO_CONFIGS.get(scenario) # None for DEFAULT falls back to BLEND + + def make_model(): + return SemanticCommSystem(cfg.d, cfg.U, cfg.H, + decoder_only=cfg.decoder_only).to(device) + + maml_model = make_model() + joint_model = make_model() + + SNR_DB = np.arange(cfg.snr_min, cfg.snr_max + 1e-6, cfg.snr_step) + + # ── 학습 ────────────────────────────────────────────────────────── + tag = f"[{scenario}]" + print(f"\n{'='*60}\n{tag} decoder_only={cfg.decoder_only}\n{'='*60}") + + trainer = MAMLTrainer(maml_model, cfg, device, np_rng, scenario_cfg) + hist_m = trainer.train() + hist_j = train_joint(joint_model, cfg, device, np_rng, scenario_cfg) + + ckpt_path = outdir / f"models_{scenario}.pt" + torch.save({"maml": maml_model.state_dict(), + "joint": joint_model.state_dict()}, ckpt_path) + print(f"체크포인트 저장 → {ckpt_path}") + + # 학습 곡선 + fig, ax = plt.subplots(figsize=(7, 4)) + ax.plot(hist_m, label='MAML outer loss', color='#1565C0', lw=1.5) + ax.plot(hist_j, label='Joint loss', color='#C62828', lw=1.5, ls='--') + ax.set_xlabel('Epoch'); ax.set_ylabel('Loss') + ax.set_title(f'Training Loss — {scenario}') + ax.legend(); ax.grid(True, alpha=0.35); fig.tight_layout() + fig.savefig(str(outdir / f"training_curves_{scenario}.png"), dpi=150) + plt.close() + + # ── 평가 ────────────────────────────────────────────────────────── + print(f"{tag} 평가 중 ...") + res_maml = evaluate_model(maml_model, cfg, device, np_rng, "rayleigh", scenario_cfg) + res_joint = evaluate_model(joint_model, cfg, device, np_rng, "rayleigh", scenario_cfg) + res_ofdma = evaluate_model(joint_model, cfg, device, np_rng, "ofdma", scenario_cfg) + res_noma = evaluate_model(joint_model, cfg, device, np_rng, "noma", scenario_cfg) + + export_results_json(scenario, SNR_DB, res_maml, res_joint, + res_ofdma, res_noma, outdir) + + # SNR=10dB 상관계수 & attention map + idx10 = int((10 - cfg.snr_min) / cfg.snr_step) + rho_j = res_joint["rho"][idx10] + rho_m = res_maml["rho"][idx10] + attn_m = get_attention_map(maml_model, 10.0, cfg, device, np_rng) + + print_summary(res_maml, res_joint, res_ofdma, res_noma, + rho_j, rho_m, attn_m, cfg) + + # 단일 시나리오 그림 저장 (DEFAULT는 기존 filename 유지) + suffix = "" if scenario == "DEFAULT" else f"_{scenario}" + fig_path = str(outdir / f"semantic_results{suffix}.png") + plot_results(SNR_DB, res_maml, res_joint, res_ofdma, res_noma, + rho_j, rho_m, attn_m, fig_path, cfg) + return res_maml, res_joint, res_ofdma, res_noma + + +def main(): + cfg = get_cfg() + torch.manual_seed(cfg.seed) + np_rng = np.random.default_rng(cfg.seed) + + if cfg.device == "auto": + device = torch.device( + "cuda" if torch.cuda.is_available() else + "mps" if torch.backends.mps.is_available() else + "cpu") + else: + device = torch.device(cfg.device) + print(f"Device: {device} | d={cfg.d}, U={cfg.U}, " + f"meta_epochs={cfg.meta_epochs}, n_mc={cfg.n_mc}, " + f"scenario={cfg.scenario}, decoder_only={cfg.decoder_only}") + + outdir = Path(cfg.outdir) + outdir.mkdir(parents=True, exist_ok=True) + + # eval_only mode (legacy) + if cfg.eval_only and cfg.ckpt: + SNR_DB = np.arange(cfg.snr_min, cfg.snr_max + 1e-6, cfg.snr_step) + scenario_cfg = SCENARIO_CONFIGS.get(cfg.scenario) + maml_model = SemanticCommSystem(cfg.d, cfg.U, cfg.H, + decoder_only=cfg.decoder_only).to(device) + joint_model = SemanticCommSystem(cfg.d, cfg.U, cfg.H, + decoder_only=cfg.decoder_only).to(device) + ck = torch.load(cfg.ckpt, map_location=device) + maml_model.load_state_dict(ck["maml"]) + joint_model.load_state_dict(ck["joint"]) + print(f"체크포인트 로드 ← {cfg.ckpt}") + res_maml = evaluate_model(maml_model, cfg, device, np_rng, "rayleigh", scenario_cfg) + res_joint = evaluate_model(joint_model, cfg, device, np_rng, "rayleigh", scenario_cfg) + res_ofdma = evaluate_model(joint_model, cfg, device, np_rng, "ofdma", scenario_cfg) + res_noma = evaluate_model(joint_model, cfg, device, np_rng, "noma", scenario_cfg) + export_results_json(cfg.scenario, SNR_DB, res_maml, res_joint, + res_ofdma, res_noma, outdir) + print(f"\n완료! → {outdir}/") + return + + # Train / eval scenarios + if cfg.scenario.upper() == "ALL": + scenarios = ['HIGH', 'LOW', 'MIX', 'HETERO', 'ASYM'] + else: + scenarios = [cfg.scenario] + + for sc in scenarios: + _run_one_scenario(sc, cfg, device, np_rng, outdir) + + print(f"\n완료! → {outdir}/") + + +if __name__ == "__main__": + main() diff --git a/legacy/plot_figures.py b/legacy/plot_figures.py new file mode 100755 index 0000000..035610f --- /dev/null +++ b/legacy/plot_figures.py @@ -0,0 +1,1044 @@ +""" +============================================================================= +plot_figures.py — Plotting-only script for the Semantic Correlation Simulation. + +Loads pre-computed CSV data from results/data/ (produced by +semantic_correlation_sim.py) and regenerates all figures (fig1–fig12, +fig10b) plus the numerical summary printout. + +Usage: + python plot_figures.py + +Requires: results/data/*.csv to exist (run semantic_correlation_sim.py first). +============================================================================= +""" + +import warnings +warnings.filterwarnings('ignore') + +import os +import numpy as np +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +from matplotlib.colors import LinearSegmentedColormap +from matplotlib.lines import Line2D as _L2D +from matplotlib.lines import Line2D + +# ── Global plot style ───────────────────────────────────────────────────────── +plt.rcParams.update({ + 'font.family': 'DejaVu Sans', + 'axes.unicode_minus': False, + 'axes.labelsize': 12, + 'axes.titlesize': 12, + 'xtick.labelsize': 10, + 'ytick.labelsize': 10, + 'legend.fontsize': 9.5, + 'figure.dpi': 150, + 'lines.linewidth': 1.8, + 'lines.markersize': 6, +}) + +# ══════════════════════════════════════════════════════════════════════════════ +# 0. Config constants (must match semantic_correlation_sim.py) +# ══════════════════════════════════════════════════════════════════════════════ +D = 64 +U = 4 +TAU = 0.45 +OUT_DIR = 'results' +DATA_DIR = f'{OUT_DIR}/data' +os.makedirs(OUT_DIR, exist_ok=True) + +SCENARIOS = { + 'HIGH': { + 'title': 'HIGH Scenario (All Users Correlated)', + 'users': ['TL-Camera (U1)', 'Autovehicle (U2)', + 'Pedestrian (U3)', 'Queue-Est. (U4)'], + 'beta_u': [0.65, 0.65, 0.60, 0.60], + 'scenes': ['traffic', 'traffic', 'traffic', 'traffic'], + 'color': '#1565C0', + }, + 'LOW': { + 'title': 'LOW Scenario (All Users Uncorrelated)', + 'users': ['TL-Camera (U1)', 'TV Viewer (U2)', + 'Music Stream (U3)', 'IoT Weather (U4)'], + 'beta_u': [0.65, 0.05, 0.05, 0.05], + 'scenes': ['traffic', 'home', 'office', 'outdoor'], + 'color': '#C62828', + }, + 'MIX': { + 'title': 'MIX Scenario (Correlated Pair + Unrelated Pair)', + 'users': ['TL-Camera (U1)', 'Autovehicle (U2)', + 'TV Viewer (U3)', 'Music Stream (U4)'], + 'beta_u': [0.65, 0.65, 0.05, 0.05], + 'scenes': ['traffic', 'traffic', 'home', 'office'], + 'color': '#2E7D32', + }, + 'HETERO': { + 'title': 'HETERO Scenario (Heterogeneous Correlation Structure)', + 'users': ['HD-Cam (U1)', 'HD-Cam (U2)', + 'LR-Sensor (U3)', 'IoT (U4)'], + 'beta_u': [0.75, 0.75, 0.45, 0.08], + 'scenes': ['traffic', 'traffic', 'traffic', 'indoor'], + 'color': '#6A1B9A', + }, + 'ASYM': { + 'title': 'ASYM Scenario (Asymmetric Semantic Relevance)', + 'users': ['U1 (beta=0.72)', 'U2 (beta=0.58)', + 'U3 (beta=0.35)', 'U4 (beta=0.12)'], + 'beta_u': [0.72, 0.58, 0.35, 0.12], + 'scenes': ['traffic', 'traffic', 'traffic', 'traffic'], + 'color': '#00695C', + }, +} + +USER_COLORS = ['#1565C0', '#2E7D32', '#C62828', '#6A1B9A'] + +MCFG = { + 'OFDMA': ('#546E7A', 's--', 1.5, 'OFDMA'), + 'NOMA-SIC': ('#E65100', '^-', 1.5, 'NOMA'), + 'MAML+Attn': ('#1565C0', 'o-', 2.4, 'UWCA (proposed)'), +} + +CMAP_RHO = LinearSegmentedColormap.from_list('rho', ['#1565C0', '#FFFFFF', '#C62828'], N=256) +CMAP_ATTN = LinearSegmentedColormap.from_list('attn', ['#F5F5F5', '#1565C0'], N=256) + +_U_COLORS = {1: '#9E9E9E', 2: '#2E7D32', 3: '#E65100', 4: '#1565C0'} + +BETA_VALUES = np.linspace(0.0, 0.9, 19) +SWEEP_SNRS = [0.0, 5.0, 10.0] +S_VALUES = [1, 2, 3, 5, 7, 10, 15] + +# Fair comparison constants +D_SRC_F = D // U # = 16 +D_CH_F = D # = 64 +TAU_FAIR = 0.85 +BETA_FAIR = 0.95 +_U_LIST_F = [1, 2, 4] + +MI_U_LIST = [1, 2, 3, 4] + +_U_LIST_12 = [1, 2, 4] +_OFDMA_CLR12 = '#37474F' +_UWCA_CLRS12 = {1: '#E65100', 2: '#2E7D32', 4: '#1565C0'} +_UWCA_MKRS12 = {1: 'o', 2: 's', 4: '^'} + + +def compute_beta_matrix(cfg: dict) -> np.ndarray: + bu = np.array(cfg['beta_u']) + sc = cfg['scenes'] + buv = np.zeros((U, U)) + for i in range(U): + for j in range(U): + if sc[i] == sc[j]: + buv[i, j] = bu[i] * bu[j] + return buv + + +def ser_total(Eh, Egt, tau=TAU) -> float: + cs = (Eh * Egt).sum(-1) + return float((cs < tau).mean()) + + +def _norm(E: np.ndarray) -> np.ndarray: + return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8) + + +# ══════════════════════════════════════════════════════════════════════════════ +# 1. Load CSV data +# ══════════════════════════════════════════════════════════════════════════════ +try: + import pandas as pd + _USE_PANDAS = True +except ImportError: + _USE_PANDAS = False + + +def _load_csv(filename): + path = os.path.join(DATA_DIR, filename) + if _USE_PANDAS: + return pd.read_csv(path) + else: + data = np.genfromtxt(path, delimiter=',', names=True, dtype=None, encoding='utf-8') + return data + + +print(f"Loading data from {DATA_DIR}/...") + +# SNR arrays +_snr_df = _load_csv('snr_db.csv') +SNR_DB = np.array(_snr_df['snr_db'] if _USE_PANDAS else _snr_df['snr_db'], dtype=float) + +_snr_f12_df = _load_csv('snr_f12.csv') +_SNR_F12 = np.array(_snr_f12_df['snr_db'] if _USE_PANDAS else _snr_f12_df['snr_db'], dtype=float) + +_mi_snrs_df = _load_csv('mi_snrs.csv') +MI_SNRS = np.array(_mi_snrs_df['snr_db'] if _USE_PANDAS else _mi_snrs_df['snr_db'], dtype=float) + +# Frequently used indices (computed from loaded SNR array) +IDX10 = int(np.argmin(np.abs(SNR_DB - 10))) +IDX4 = int(np.argmin(np.abs(SNR_DB - 4))) +IDX16 = int(np.argmin(np.abs(SNR_DB - 16))) +mask = ~np.eye(U, dtype=bool) +BETAS2 = BETA_VALUES ** 2 + +# --- ser_scenarios.csv → results dict --- +_ser_df = _load_csv('ser_scenarios.csv') +results = {} +for sk in SCENARIOS: + results[sk] = {} + for m in ['OFDMA', 'NOMA-SIC', 'MAML+Attn']: + if _USE_PANDAS: + _sub = _ser_df[(_ser_df['scenario'] == sk) & (_ser_df['method'] == m)].sort_values('snr_db') + _ser_arr = _sub['ser'].values if len(_sub) > 0 else np.zeros(len(SNR_DB)) + else: + _mask_s = (_ser_df['scenario'].astype(str) == sk) & (_ser_df['method'].astype(str) == m) + _sub = _ser_df[_mask_s] + _ser_arr = np.array([_sub['ser'][i] for i in range(len(_sub['ser']))], dtype=float) + results[sk][m] = {'ser': _ser_arr} + +# --- ser_per_user_mix.csv → results['MIX'] sp arrays --- +_puser_df = _load_csv('ser_per_user_mix.csv') +for m in ['OFDMA', 'NOMA-SIC', 'MAML+Attn']: + _sp = np.zeros((len(SNR_DB), U)) + for ui in range(U): + if _USE_PANDAS: + _sub = _puser_df[(_puser_df['method'] == m) & (_puser_df['user'] == ui)].sort_values('snr_db') + _sp[:, ui] = _sub['ser'].values + else: + _mask_s = (_puser_df['method'].astype(str) == m) & (_puser_df['user'] == ui) + _sub_ser = _puser_df['ser'][_mask_s] + _sp[:, ui] = np.array(list(_sub_ser), dtype=float) + results['MIX'][m]['sp'] = _sp + +# --- attn_heatmaps.csv → results[sk]['_attn_m'] and '_beta_mat' --- +_attn_df = _load_csv('attn_heatmaps.csv') +for sk in ['HIGH', 'LOW', 'MIX']: + am = np.zeros((U, U)) + if _USE_PANDAS: + _sub = _attn_df[_attn_df['scenario'] == sk] + for _, row in _sub.iterrows(): + am[int(row['row']), int(row['col'])] = float(row['alpha']) + else: + _mask_s = _attn_df['scenario'].astype(str) == sk + _rows_idx = np.where(_mask_s)[0] + for idx in _rows_idx: + am[int(_attn_df['row'][idx]), int(_attn_df['col'][idx])] = float(_attn_df['alpha'][idx]) + results[sk]['_attn_m'] = am + results[sk]['_beta_mat'] = compute_beta_matrix(SCENARIOS[sk]) + results[sk]['_rho_m'] = np.eye(U) # not saved; placeholder (not used in plots) + +# --- beta_sweep.csv → beta_sweeps dict --- +_bsweep_df = _load_csv('beta_sweep.csv') +beta_sweeps = {} +for snr_lbl in SWEEP_SNRS: + _gm = np.zeros(len(BETA_VALUES)) + for bi in range(len(BETA_VALUES)): + if _USE_PANDAS: + _sub = _bsweep_df[ + (np.abs(_bsweep_df['snr_label'] - snr_lbl) < 1e-6) & + (np.abs(_bsweep_df['beta_sq'] - BETAS2[bi]) < 1e-9) + ] + if len(_sub) > 0: + _gm[bi] = float(_sub['gain_maml'].values[0]) + else: + _mask_s = (np.abs(_bsweep_df['snr_label'].astype(float) - snr_lbl) < 1e-6) & \ + (np.abs(_bsweep_df['beta_sq'].astype(float) - BETAS2[bi]) < 1e-9) + _idx = np.where(_mask_s)[0] + if len(_idx) > 0: + _gm[bi] = float(_bsweep_df['gain_maml'][_idx[0]]) + beta_sweeps[snr_lbl] = {'gain_maml': _gm} +beta_sweep = beta_sweeps[10.0] + +# --- ablation.csv → ablation dict --- +_abl_df = _load_csv('ablation.csv') +if _USE_PANDAS: + _abl_main = _abl_df[_abl_df['S'] != 999].sort_values('S') + _abl_ideal = _abl_df[_abl_df['S'] == 999] + ablation = { + 'S_values': list(_abl_main['S'].values.astype(int)), + 'ser': _abl_main['ser'].values, + 'ser_ideal': float(_abl_ideal['ser'].values[0]), + } +else: + _s_vals = _abl_df['S'].astype(int) + _ser_vals = _abl_df['ser'].astype(float) + _main_mask = _s_vals != 999 + ablation = { + 'S_values': list(_s_vals[_main_mask]), + 'ser': np.array(list(_ser_vals[_main_mask])), + 'ser_ideal': float(_ser_vals[~_main_mask][0]), + } + +# --- u_variation_high.csv → u_var_results --- +_uvar_high_df = _load_csv('u_variation_high.csv') +u_var_results = {} +for U_val in [1, 2, 3, 4]: + u_var_results[U_val] = {} + for m in ['OFDMA', 'UWCA']: + if _USE_PANDAS: + _sub = _uvar_high_df[(_uvar_high_df['U'] == U_val) & + (_uvar_high_df['method'] == m)].sort_values('snr_db') + _sarr = _sub['ser'].values + else: + _mask_s = (_uvar_high_df['U'].astype(int) == U_val) & \ + (_uvar_high_df['method'].astype(str) == m) + _sarr = np.array(list(_uvar_high_df['ser'][_mask_s]), dtype=float) + u_var_results[U_val][m] = {'ser': _sarr} + +# --- u_variation_f12.csv → u_var_f12_09, u_var_f12_05, u_var_f12_01 --- +_uvar_f12_df = _load_csv('u_variation_f12.csv') + +def _load_uvar_f12(beta_val): + _d = {} + for U_val in [1, 2, 3, 4]: + _d[U_val] = {} + for m in ['OFDMA', 'UWCA']: + if _USE_PANDAS: + _sub = _uvar_f12_df[ + (np.abs(_uvar_f12_df['beta'] - beta_val) < 1e-6) & + (_uvar_f12_df['U'] == U_val) & + (_uvar_f12_df['method'] == m) + ].sort_values('snr_db') + _sarr = _sub['ser'].values + else: + _mask_s = (np.abs(_uvar_f12_df['beta'].astype(float) - beta_val) < 1e-6) & \ + (_uvar_f12_df['U'].astype(int) == U_val) & \ + (_uvar_f12_df['method'].astype(str) == m) + _sarr = np.array(list(_uvar_f12_df['ser'][_mask_s]), dtype=float) + _d[U_val][m] = {'ser': _sarr} + return _d + +u_var_f12_09 = _load_uvar_f12(0.9) +u_var_f12_05 = _load_uvar_f12(0.5) +u_var_f12_01 = _load_uvar_f12(0.1) + +# --- u_variation_low.csv → u_var_low_results --- +_uvar_low_df = _load_csv('u_variation_low.csv') +u_var_low_results = {} +for U_val in [1, 2, 3, 4]: + u_var_low_results[U_val] = {} + for m in ['OFDMA', 'UWCA']: + if _USE_PANDAS: + _sub = _uvar_low_df[(_uvar_low_df['U'] == U_val) & + (_uvar_low_df['method'] == m)].sort_values('snr_db') + _sarr = _sub['ser'].values + else: + _mask_s = (_uvar_low_df['U'].astype(int) == U_val) & \ + (_uvar_low_df['method'].astype(str) == m) + _sarr = np.array(list(_uvar_low_df['ser'][_mask_s]), dtype=float) + u_var_low_results[U_val][m] = {'ser': _sarr} + +# --- mi_bounds.csv → mi_bounds dict --- +_mi_df = _load_csv('mi_bounds.csv') +mi_bounds = {} +for U_val in MI_U_LIST: + if _USE_PANDAS: + _sub = _mi_df[_mi_df['U'] == U_val].sort_values('snr_db') + _rl = float(_sub['ratio_low_snr'].values[0]) + _mbd = { + 'I_ofdma': _sub['I_ofdma'].values, + 'I_uwca': _sub['I_uwca'].values, + 'snr_db': _sub['snr_db'].values, + 'U': U_val, + 'ratio_low_snr': _rl, + } + else: + _mask_s = _mi_df['U'].astype(int) == U_val + _rl = float(_mi_df['ratio_low_snr'][_mask_s][0]) + _mbd = { + 'I_ofdma': np.array(list(_mi_df['I_ofdma'][_mask_s]), dtype=float), + 'I_uwca': np.array(list(_mi_df['I_uwca'][_mask_s]), dtype=float), + 'snr_db': np.array(list(_mi_df['snr_db'][_mask_s]), dtype=float), + 'U': U_val, + 'ratio_low_snr': _rl, + } + mi_bounds[U_val] = _mbd + +# --- fair_comparison.csv → fair_results --- +_fair_df = _load_csv('fair_comparison.csv') +fair_results = {} +for U_val in _U_LIST_F: + fair_results[U_val] = {} + for m in ['OFDMA', 'UWCA']: + if _USE_PANDAS: + _sub = _fair_df[(_fair_df['U'] == U_val) & + (_fair_df['method'] == m)].sort_values('snr_db') + _sarr = _sub['ser'].values + else: + _mask_s = (_fair_df['U'].astype(int) == U_val) & \ + (_fair_df['method'].astype(str) == m) + _sarr = np.array(list(_fair_df['ser'][_mask_s]), dtype=float) + fair_results[U_val][m.upper()] = _sarr + +print("Data loaded successfully.") +print() + +# Helper for loading trained overlay results (from maml_semantic.py JSON export) +import json + +def load_trained_results(scenario_key: str) -> dict: + path = os.path.join(OUT_DIR, f"trained_{scenario_key}.json") + if not os.path.isfile(path): + return None + with open(path) as f: + d = json.load(f) + return {k: np.array(v) if isinstance(v, list) else v for k, v in d.items()} + +# ══════════════════════════════════════════════════════════════════════════════ +# 2. Helper plotting functions +# ══════════════════════════════════════════════════════════════════════════════ +def _plot_ser(ax, sk, annotate=True): + res = results[sk] + for m, (c, mk, lw, lb) in MCFG.items(): + ax.semilogy(SNR_DB, res[m]['ser'], mk, lw=lw, color=c, label=lb) + if annotate: + d10 = res['OFDMA']['ser'][IDX10] - res['MAML+Attn']['ser'][IDX10] + if d10 > 0.005: + ax.annotate(f'\u0394={d10:.3f}', + xy=(10, res['MAML+Attn']['ser'][IDX10]), + xytext=(13.5, res['MAML+Attn']['ser'][IDX10] * 4.5), + fontsize=9, color='#1565C0', + arrowprops=dict(arrowstyle='->', color='#1565C0', lw=1.1)) + ax.set_xlabel('SNR (dB)'); ax.set_ylabel('SER') + ax.legend(loc='lower left'); ax.grid(True, alpha=0.3) + ax.set_xlim(0, 20) + + +def _style_ax(ax): + ax.set_facecolor('white') + + +def _ieee_label(ax, letter, name=None, fontsize=10): + """Place IEEE-style sub-figure label below x-axis, panel bottom-centre. + If name is given, appends the scenario name: e.g. '(a) HIGH'.""" + txt = f'{letter} {name}' if name else letter + ax.text(0.5, -0.20, txt, transform=ax.transAxes, + ha='center', va='top', fontsize=fontsize, fontweight='bold') + + +def _overlay_trained(ax, scenario_key: str): + """Overlay trained (decoder-only MAML) results as hollow markers if JSON exists.""" + tr = load_trained_results(scenario_key) + if tr is None: + return + snr = tr['snr_db'] + ax.semilogy(snr, tr['maml_ser'], 'o', ms=7, mfc='none', mec='#1565C0', + mew=1.8, label='UWCA (Trained)', zorder=5) + + +def _snr_at_ser(ser_arr, snr_arr, target=0.30): + """Interpolate SNR where SER crosses target (descending).""" + for k in range(len(ser_arr) - 1): + if ser_arr[k] >= target >= ser_arr[k + 1]: + t = (target - ser_arr[k]) / (ser_arr[k + 1] - ser_arr[k] + 1e-12) + return snr_arr[k] + t * (snr_arr[k + 1] - snr_arr[k]) + return None # doesn't cross + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 1 — SER vs SNR: HIGH, LOW, MIX (3-panel, 1 row) +# ══════════════════════════════════════════════════════════════════════════════ +fig1, axes1 = plt.subplots(1, 3, figsize=(18, 6.0)) +fig1.patch.set_facecolor('#F8F9FA') + +_FIG1_SCENARIOS = ['HIGH', 'LOW', 'MIX'] +_FIG1_LETTERS = ['(a)', '(b)', '(c)'] + +# y-axis range based on HIGH scenario minimum (tight fit, no wasted whitespace) +_high_res = results['HIGH'] +_high_min = min(float(np.min(_high_res[m]['ser'])) for m in MCFG) +_ymin = _high_min * 0.75 # tight margin below HIGH min (~0.048 → ymin≈0.036) +_ymax = 1.2 + +for ax, sk, letter in zip(axes1, _FIG1_SCENARIOS, _FIG1_LETTERS): + _style_ax(ax) + res = results[sk] + for m, (c, mk, lw, lb) in MCFG.items(): + ax.semilogy(SNR_DB, res[m]['ser'], mk, lw=lw, color=c, label=lb) + _overlay_trained(ax, sk) # overlay trained results if available + + # ── Scenario-specific annotations ─────────────────────────────── + if sk == 'LOW': + # Place text in center area below curve cluster + ax.text(0.50, 0.38, + 'OFDMA $\\equiv$ UWCA\n($\\beta_{u,v}\\approx 0$)', + transform=ax.transAxes, + fontsize=14, color='#546E7A', ha='center', va='center') + + d10 = res['OFDMA']['ser'][IDX10] - res['MAML+Attn']['ser'][IDX10] + if d10 > 0.005 and sk != 'LOW': + y_uwca = res['MAML+Attn']['ser'][IDX10] + y_ofdma = res['OFDMA']['ser'][IDX10] + ax.annotate('', xy=(10, y_ofdma), xytext=(10, y_uwca), + arrowprops=dict(arrowstyle='<->', color='#1565C0', lw=1.2)) + y_mid = np.exp((np.log(y_uwca) + np.log(y_ofdma)) / 2) + _txt_pos = (0.85, 0.82) if sk == 'HIGH' else (0.30, 0.62) + ax.annotate(f'$\\Delta$={d10:.3f}', + xy=(10, y_mid), + xytext=_txt_pos, textcoords='axes fraction', + fontsize=14, color='#1565C0', ha='center', va='center', + arrowprops=dict(arrowstyle='->', color='#1565C0', lw=1.2, + connectionstyle='arc3,rad=0.2'), + bbox=dict(boxstyle='round,pad=0.3', fc='white', alpha=0.85, + ec='#1565C0', lw=0.8)) + + ax.set_xlabel('SNR (dB)', fontsize=17) + if sk == 'HIGH': + ax.set_ylabel('SER', fontsize=17) + ax.tick_params(labelsize=16) + ax.legend(loc='lower left', fontsize=14); ax.grid(True, alpha=0.3) + ax.set_xlim(0, 20) + ax.set_ylim(_ymin, _ymax) + _ieee_label(ax, letter, name=sk, fontsize=17) + +fig1.tight_layout() +fig1.subplots_adjust(bottom=0.20, top=0.95) +for ax in axes1: + ax.set_position([ax.get_position().x0, 0.200, 5.1604/18, 4.5000/6.0]) +fig1.savefig(f'{OUT_DIR}/fig1_ser_high_low_mix.png', dpi=150, bbox_inches='tight', facecolor='#F8F9FA') +fig1.savefig(f'{OUT_DIR}/fig1_ser_high_low_mix.pdf', bbox_inches='tight', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig1_ser_high_low_mix.png/.pdf") + + +# FIGURE 2 — removed (HETERO/ASYM are variants of MIX; 3 scenarios suffice) + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 3 — Per-user SER: MIX scenario (single panel) +# ══════════════════════════════════════════════════════════════════════════════ +fig3, ax3 = plt.subplots(1, 1, figsize=(6.27, 6.0)) +fig3.patch.set_facecolor('#F8F9FA') + +_style_ax(ax3) +res = results['MIX'] +# UWCA: average symmetric-β pairs (same β_u → same theoretical SER) +_groups = [ + (slice(0, 2), '#1565C0', r'Correlated'), + (slice(2, 4), '#C62828', r'Uncorrelated'), +] +for sl, col, lbl in _groups: + ax3.semilogy(SNR_DB, res['MAML+Attn']['sp'][:, sl].mean(axis=1), + 'o-', lw=1.8, color=col, label=f'{lbl} — UWCA') +# OFDMA: β-independent → single curve averaged over all users +ax3.semilogy(SNR_DB, res['OFDMA']['sp'].mean(axis=1), + 's--', lw=1.2, color='#546E7A', label='OFDMA (reference)') +# NOMA: power-allocation-dependent → single curve averaged over all users +ax3.semilogy(SNR_DB, res['NOMA-SIC']['sp'].mean(axis=1), + '^-', lw=1.2, color='#E65100', label='NOMA (reference)') +ax3.set_xlabel('SNR (dB)', fontsize=17); ax3.set_ylabel('Per-user SER', fontsize=17) +ax3.tick_params(labelsize=16) +ax3.legend(loc='lower left', fontsize=14); ax3.grid(True, alpha=0.3) +ax3.set_xlim(0, 20) + +fig3.tight_layout() +fig3.subplots_adjust(bottom=0.20, top=0.95) +ax3.set_position([ax3.get_position().x0, 0.200, 5.1604/6.27, 4.5000/6.0]) +fig3.savefig(f'{OUT_DIR}/fig3_per_user_ser.png', dpi=150, bbox_inches='tight', facecolor='#F8F9FA') +fig3.savefig(f'{OUT_DIR}/fig3_per_user_ser.pdf', bbox_inches='tight', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig3_per_user_ser.png/.pdf") + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 4 — beta sweep (SER gain vs beta_uv, multi-SNR) +# ══════════════════════════════════════════════════════════════════════════════ +_SWEEP_STYLES = { + 0.0: ('#C62828', 's--', 'SNR = 0 dB'), + 5.0: ('#E65100', '^-.', 'SNR = 5 dB'), + 10.0: ('#1565C0', 'o-', 'SNR = 10 dB'), +} +_FILL_COLORS = {0.0: '#C62828', 5.0: '#E65100', 10.0: '#1565C0'} + +fig4, ax4 = plt.subplots(figsize=(6.27, 6.0)) +fig4.patch.set_facecolor('#F8F9FA') +_style_ax(ax4) + +for snr in SWEEP_SNRS: + clr, mk, lbl = _SWEEP_STYLES[snr] + gain = beta_sweeps[snr]['gain_maml'] + ax4.plot(BETAS2, gain, mk, lw=2.0, color=clr, label=lbl, markersize=5) + ax4.fill_between(BETAS2, 0, gain, alpha=0.07, color=_FILL_COLORS[snr]) + +ax4.axhline(0, color='gray', lw=0.8, ls=':') + +ax4.set_xlabel('Semantic relevance coefficient $\\beta_{u,v} = \\beta_u \\cdot \\beta_v$', fontsize=17) +ax4.set_ylabel('SER gain over OFDMA', fontsize=17) +ax4.tick_params(labelsize=16) +ax4.legend(loc='upper left', fontsize=14) +ax4.grid(True, alpha=0.3) +ax4.set_xlim(-0.01, 0.82) +fig4.tight_layout() +fig4.subplots_adjust(bottom=0.20, top=0.95) +ax4.set_position([ax4.get_position().x0, 0.200, 5.1604/6.27, 4.5000/6.0]) +fig4.savefig(f'{OUT_DIR}/fig4_beta_sweep.png', dpi=150, bbox_inches='tight', facecolor='#F8F9FA') +fig4.savefig(f'{OUT_DIR}/fig4_beta_sweep.pdf', bbox_inches='tight', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig4_beta_sweep.png/.pdf") + + +# FIGURE 5 — removed (bar chart at 10 dB is redundant with fig1 SER curves) + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 6 — Attention weight matrices: HIGH, LOW, MIX (3-panel, 1 row) +# ══════════════════════════════════════════════════════════════════════════════ +fig6, axes6 = plt.subplots(1, 3, figsize=(18, 6.0)) +fig6.patch.set_facecolor('#F8F9FA') + +for ax, sk, letter in zip(axes6, ['HIGH', 'LOW', 'MIX'], ['(a)', '(b)', '(c)'],): + _style_ax(ax) + am = results[sk]['_attn_m'] + beta_mat = results[sk]['_beta_mat'] + im = ax.imshow(am, cmap=CMAP_ATTN, vmin=0, vmax=1.0, aspect='auto') + labels = SCENARIOS[sk]['users'] + short_labels = [f'U{i+1}' for i in range(U)] + ax.set_xticks(range(U)); ax.set_yticks(range(U)) + ax.set_xticklabels(short_labels, fontsize=16) + ax.set_yticklabels(short_labels, fontsize=16) + for i in range(U): + for j in range(U): + v = am[i, j] + ax.text(j, i, f'{v:.2f}', ha='center', va='center', fontsize=16, + fontweight='bold', + color='white' if v > am.max() * 0.55 else '#0D1B3E') + # Highlight high-beta pairs + for i in range(U): + for j in range(U): + if i != j and beta_mat[i, j] > 0.1: + ax.add_patch(plt.Rectangle((j - 0.5, i - 0.5), 1, 1, + fill=False, edgecolor='#FFD600', lw=2.5)) + ax.set_xlabel('Source user $i$', fontsize=17) + if sk == 'HIGH': + ax.set_ylabel('Query user $u$', fontsize=17) + _ieee_label(ax, letter, name=sk, fontsize=17) + +# Colorbar attached to panel (c) only +cbar = fig6.colorbar(im, ax=axes6[2], fraction=0.046, pad=0.04) +cbar.set_label('Attention weight $\\alpha_{u,i}$', fontsize=14) +cbar.ax.tick_params(labelsize=13) +fig6.tight_layout() +fig6.subplots_adjust(bottom=0.20, top=0.95) +fig6.savefig(f'{OUT_DIR}/fig6_attn_heatmaps.png', dpi=150, bbox_inches='tight', facecolor='#F8F9FA') +fig6.savefig(f'{OUT_DIR}/fig6_attn_heatmaps.pdf', bbox_inches='tight', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig6_attn_heatmaps.png/.pdf") + + +# FIGURE 7 — removed (|rho_off| values incorporated into fig6 caption) + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 8 — MAML inner-loop steps S ablation (single panel) +# ══════════════════════════════════════════════════════════════════════════════ +fig8, ax8 = plt.subplots(figsize=(6.27, 6.0)) +fig8.patch.set_facecolor('#F8F9FA') +_style_ax(ax8) + +ax8.plot(S_VALUES, ablation['ser'], 'o-', lw=2.2, ms=7, + color='#1565C0', label='UWCA ($S$ steps)') +ax8.axhline(ablation['ser_ideal'], color='#1565C0', lw=1.4, ls='--', alpha=0.65, + label=f'UWCA ($S\\to\\infty$) = {ablation["ser_ideal"]:.3f}') +ax8.axhline(results['MIX']['OFDMA']['ser'][IDX10], + color='#546E7A', lw=1.2, ls=':', alpha=0.8, + label=f'OFDMA (Analytical) = {results["MIX"]["OFDMA"]["ser"][IDX10]:.3f}') + +best_idx = int(np.argmin(ablation['ser'])) +ax8.annotate(f'Optimal $S$={S_VALUES[best_idx]}', + xy=(S_VALUES[best_idx], ablation['ser'][best_idx]), + xytext=(S_VALUES[best_idx] - 4.5, ablation['ser'][best_idx] + 0.04), + fontsize=14, color='#1565C0', + arrowprops=dict(arrowstyle='->', color='#1565C0', lw=1.1), + bbox=dict(boxstyle='round,pad=0.2', fc='white', alpha=0.85, ec='none')) + +ax8.set_xlabel('Number of inner-loop steps $S$', fontsize=17) +ax8.set_ylabel('SER', fontsize=17) +ax8.tick_params(labelsize=16) +ax8.set_xticks(S_VALUES); ax8.legend(loc='upper right', fontsize=14); ax8.grid(True, alpha=0.3) +fig8.tight_layout() +fig8.subplots_adjust(bottom=0.20, top=0.95) +ax8.set_position([ax8.get_position().x0, 0.200, 5.1604/6.27, 4.5000/6.0]) +fig8.savefig(f'{OUT_DIR}/fig8_ablation.png', dpi=150, bbox_inches='tight', facecolor='#F8F9FA') +fig8.savefig(f'{OUT_DIR}/fig8_ablation.pdf', bbox_inches='tight', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig8_ablation.png/.pdf") + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 9 — SER vs SNR: U-user scaling, single panel (U = 1, 2, 4) +# ══════════════════════════════════════════════════════════════════════════════ +_U_LIST_9 = [1, 2, 4] + +fig9, ax9 = plt.subplots(figsize=(6.27, 6.0)) +fig9.patch.set_facecolor('#F8F9FA') +_style_ax(ax9) + +for U_val in _U_LIST_9: + clr = _U_COLORS[U_val] + res_u = u_var_results[U_val] + # OFDMA: only show for U=4 (representative) + if U_val == 4: + ax9.semilogy(SNR_DB, res_u['OFDMA']['ser'], '--', lw=1.6, ms=0, + color='#546E7A', alpha=0.75) + ax9.semilogy(SNR_DB, res_u['UWCA']['ser'], '-', lw=2.2, ms=0, + color=clr) + +# Unified legend: OFDMA + UWCA-SE per U value +legend_handles = [ + _L2D([0],[0], color='#546E7A', lw=1.6, ls='--', alpha=0.75, label='OFDMA (Analytical)'), + _L2D([0],[0], color=_U_COLORS[1], lw=2.2, ls='-', label='UWCA ($U=1$)'), + _L2D([0],[0], color=_U_COLORS[2], lw=2.2, ls='-', label='UWCA ($U=2$)'), + _L2D([0],[0], color=_U_COLORS[4], lw=2.2, ls='-', label='UWCA ($U=4$)'), +] +ax9.legend(handles=legend_handles, loc='lower left', fontsize=14) + +ax9.axhline(TAU, color='gray', lw=0.8, ls=':', alpha=0.6) +ax9.text(0.5, TAU * 1.18, f'$\\tau={TAU}$', fontsize=14, color='gray') +ax9.set_xlabel('SNR (dB)', fontsize=17); ax9.set_ylabel('SER', fontsize=17) +ax9.tick_params(labelsize=16) +ax9.grid(True, alpha=0.3); ax9.set_xlim(0, 20) + +fig9.tight_layout() +fig9.subplots_adjust(bottom=0.15) +ax9.set_position([ax9.get_position().x0, 0.150, 5.1604/6.27, 4.9500/6.0]) +fig9.savefig(f'{OUT_DIR}/fig9_u_variation_ser.png', dpi=150, facecolor='#F8F9FA') +fig9.savefig(f'{OUT_DIR}/fig9_u_variation_ser.pdf', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig9_u_variation_ser.png/.pdf") + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 10 — Mutual Information Bounds vs SNR (analytical, multi-U) +# ══════════════════════════════════════════════════════════════════════════════ +fig10, (ax10a, ax10b) = plt.subplots(1, 2, figsize=(12, 6)) +fig10.patch.set_facecolor('#F8F9FA') + +# --- 10a: I vs SNR for each U (OFDMA-SE vs UWCA-SE, ergodic Rayleigh) --- +_style_ax(ax10a) +for U_val in MI_U_LIST: + mb = mi_bounds[U_val] + clr = _U_COLORS[U_val] + ax10a.plot(MI_SNRS, mb['I_ofdma'], '--', lw=1.6, color=clr, alpha=0.6) + ax10a.plot(MI_SNRS, mb['I_uwca'], '-', lw=2.2, color=clr, + label=f'U={U_val}') + +_style_handles = [ + Line2D([0], [0], color='k', lw=2.2, ls='-', label='UWCA (Analytical)'), + Line2D([0], [0], color='k', lw=1.6, ls='--', alpha=0.6, label='OFDMA (Analytical)'), +] +_color_handles = [Line2D([0],[0], color=_U_COLORS[u], lw=2.2, label=f'U={u}') + for u in MI_U_LIST] +leg_style = ax10a.legend(handles=_style_handles, loc='upper left', fontsize=8.5) +ax10a.legend(handles=_color_handles, loc='center left', fontsize=9, + bbox_to_anchor=(0.0, 0.55)) +ax10a.add_artist(leg_style) +ax10a.set_xlabel('SNR (dB)') +ax10a.set_ylabel('Ergodic MI (bits / ch. use / user, Rayleigh)') +ax10a.grid(True, alpha=0.3) +ax10a.set_xlim(0, 20) +_ieee_label(ax10a, '(a)') + +# --- 10b: MI ratio I_UWCA / I_OFDMA vs SNR — correct asymptote annotation --- +# TRUE behavior: ratio peaks at SNR→0 [ = 1+(U-1)β² ] and decreases to 1 at SNR→∞ +# because I_cross = C_erg(SNR/D)−C_erg((1-β²)SNR/D) → log₂(1/(1-β²)) = const +# while I_OFDMA grows without bound ⟹ ratio → 1. +_style_ax(ax10b) +for U_val in MI_U_LIST: + mb = mi_bounds[U_val] + clr = _U_COLORS[U_val] + safe = np.where(mb['I_ofdma'] > 1e-6, mb['I_ofdma'], np.nan) + ratio = mb['I_uwca'] / safe + ax10b.plot(MI_SNRS, ratio, '-', lw=2.2, color=clr, label=f'U={U_val}') + # Correct low-SNR limit: 1 + (U-1)·β² + low_lim = mb['ratio_low_snr'] + ax10b.axhline(low_lim, color=clr, lw=1.8, ls='--', alpha=0.85) + ax10b.text(20.4, low_lim + 0.07, + f'$1\!+\!{U_val-1}\\beta^2$={low_lim:.2f}', + fontsize=7.5, color=clr, va='bottom') + +ax10b.axhline(1.0, color='gray', lw=1.8, ls='--', alpha=0.9) +ax10b.text(0.3, 1.04, 'High-SNR limit = 1', fontsize=8, color='gray', va='bottom') +ax10b.set_xlabel('SNR (dB)') +ax10b.set_ylabel(r'Ergodic MI ratio $I_{\rm UWCA} / I_{\rm OFDMA}$') +ax10b.legend(fontsize=9, loc='upper right') +ax10b.grid(True, alpha=0.3) +ax10b.set_xlim(0, 20); ax10b.set_ylim(0.8, 4.5) +# Annotation: explain the monotone-decreasing behaviour +ax10b.text(0.98, 0.97, + 'Ratio peaks at SNR$\\to$0: $1+(U\\!-\\!1)\\beta^2$\n' + 'Decreases monotonically; High-SNR limit = 1\n' + '(cross-block SINR saturates at $\\beta^2/(1\\!-\\!\\beta^2)$)', + transform=ax10b.transAxes, fontsize=7.5, ha='right', va='top', + bbox=dict(boxstyle='round,pad=0.3', fc='#FFF9C4', alpha=0.9, ec='#FBC02D', lw=0.8)) +_ieee_label(ax10b, '(b)') + +fig10.tight_layout() +fig10.subplots_adjust(bottom=0.15) +fig10.savefig(f'{OUT_DIR}/fig10_mutual_information.png', dpi=150, + bbox_inches='tight', facecolor='#F8F9FA') +fig10.savefig(f'{OUT_DIR}/fig10_mutual_information.pdf', + bbox_inches='tight', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig10_mutual_information.png/.pdf") + +# ── fig10b: panel (b) only — MI ratio, standalone for paper ────────────────── +fig10b, ax10b_s = plt.subplots(figsize=(6.27, 6.0)) +fig10b.patch.set_facecolor('#F8F9FA') +_style_ax(ax10b_s) +for U_val in MI_U_LIST: + mb = mi_bounds[U_val] + clr = _U_COLORS[U_val] + safe = np.where(mb['I_ofdma'] > 1e-6, mb['I_ofdma'], np.nan) + ratio = mb['I_uwca'] / safe + ax10b_s.plot(MI_SNRS, ratio, '-', lw=2.2, color=clr, label=f'$U={U_val}$') + low_lim = mb['ratio_low_snr'] + ax10b_s.axhline(low_lim, color=clr, lw=1.8, ls='--', alpha=0.85) + ax10b_s.text(19.5, low_lim + 0.07, + f'$1\\!+\\!{U_val-1}\\beta^2$={low_lim:.2f}', + fontsize=14, color=clr, va='bottom', ha='right') +ax10b_s.axhline(1.0, color='gray', lw=1.8, ls='--', alpha=0.9) +ax10b_s.text(0.3, 1.12, 'High-SNR limit = 1', fontsize=14, color='gray', va='bottom') +ax10b_s.set_xlabel('SNR (dB)', fontsize=17) +ax10b_s.set_ylabel('MI ratio', fontsize=17) +ax10b_s.legend(fontsize=14, loc='upper left') +ax10b_s.tick_params(labelsize=16) +ax10b_s.grid(True, alpha=0.3) +ax10b_s.set_xlim(0, 20); ax10b_s.set_ylim(0.8, 4.5) +fig10b.tight_layout() +fig10b.subplots_adjust(bottom=0.15) +ax10b_s.set_position([ax10b_s.get_position().x0, 0.150, 5.1604/6.27, 4.9500/6.0]) +fig10b.savefig(f'{OUT_DIR}/fig10b_mi_ratio.png', dpi=150, facecolor='#F8F9FA') +fig10b.savefig(f'{OUT_DIR}/fig10b_mi_ratio.pdf', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig10b_mi_ratio.png/.pdf") + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 11 — Fair Comparison: fixed d_src = D/U_MAX = 16, D_ch = 64 +# ══════════════════════════════════════════════════════════════════════════════ +# Key difference from fig9 (unfair): +# UNFAIR (fig9): source e_u ∈ ℝ^64, masked to 16 active dims → OFDMA cos_sim ≤ 0.5 (structural ceiling) +# FAIR (fig11): source e_u ∈ ℝ^16, placed in own block → OFDMA cos_sim → 1.0 (no ceiling) +# +# Power normalization: noise_std fixed to per-user reference power (1 user, 16-dim in 64-dim ch) +# → OFDMA performance is CONSTANT across U (each user always recovers clean 16-dim block) +# → UWCA-SE improves with U (aggregates more correlated blocks, noise averaging ∝ 1/U) +# → Gain of UWCA-SE over OFDMA = U × SNR advantage +# +# TAU_FAIR = 0.85 (higher threshold since both methods can now exceed cos_sim = 0.5) + +fig11, (ax11a, ax11b) = plt.subplots(1, 2, figsize=(12, 6)) +fig11.patch.set_facecolor('#F8F9FA') + +# Left panel: SER vs SNR curves +_style_ax(ax11a) +for _U in _U_LIST_F: + _clr = _U_COLORS[_U] + _res = fair_results[_U] + ax11a.semilogy(SNR_DB, _res['OFDMA'], '--', lw=1.5, color=_clr, alpha=0.6) + ax11a.semilogy(SNR_DB, _res['UWCA'], '-', lw=2.2, color=_clr) + # end-of-curve label for UWCA + _last = _res['UWCA'][-1] + if _last > 1e-5: + ax11a.text(20.3, _last, f'$U={_U}$', fontsize=9, color=_clr, va='center') + +ax11a.axhline(TAU_FAIR, color='gray', lw=0.8, ls=':', alpha=0.6) +ax11a.text(0.5, TAU_FAIR * 1.07, f'$\\tau={TAU_FAIR}$', fontsize=8, color='gray') + +_lh_fair = [ + _L2D([0],[0], color='k', lw=1.5, ls='--', alpha=0.6, label='OFDMA (Analytical)'), + _L2D([0],[0], color='k', lw=2.2, ls='-', label='UWCA (Analytical)'), +] + [ + _L2D([0],[0], color=_U_COLORS[u], lw=2.2, label=f'$U={u}$') for u in _U_LIST_F +] +ax11a.legend(handles=_lh_fair, loc='lower left', fontsize=9) +ax11a.set_xlabel('SNR (dB)') +ax11a.set_ylabel('SER') +ax11a.set_title(r'(a) Fair: $d_{\rm src}=16$, $D_{\rm ch}=64$, $\tau=0.85$', + fontsize=10, pad=6) +ax11a.grid(True, alpha=0.3) +ax11a.set_xlim(0, 20) + +# Right panel: SNR gain vs U at SER = 0.30 (shows gain direction) +_style_ax(ax11b) + +_target_ser = 0.30 +_u_vals_plot = [1, 2, 4] + +# Unfair gains (from u_var_results, using TAU=0.45) +_gain_unfair = [] +for _U in _u_vals_plot: + _r = u_var_results[_U] + _so = _snr_at_ser(_r['OFDMA']['ser'], SNR_DB, _target_ser) + _sw = _snr_at_ser(_r['UWCA']['ser'], SNR_DB, _target_ser) + _gain_unfair.append((_so - _sw) if (_so is not None and _sw is not None) else 0.0) + +# Fair gains (from fair_results, using TAU_FAIR=0.85) +_gain_fair = [] +for _U in _u_vals_plot: + _r = fair_results[_U] + _so = _snr_at_ser(_r['OFDMA'], SNR_DB, _target_ser) + _sw = _snr_at_ser(_r['UWCA'], SNR_DB, _target_ser) + _gain_fair.append((_so - _sw) if (_so is not None and _sw is not None) else 0.0) + +_x = np.array(_u_vals_plot, dtype=float) +_bar_w = 0.3 +ax11b.bar(_x - _bar_w/2, _gain_unfair, _bar_w, label='Unfair (current, $\\tau=0.45$)', + color='#546E7A', alpha=0.75) +ax11b.bar(_x + _bar_w/2, _gain_fair, _bar_w, label='Fair ($d_{\\rm src}=16$, $\\tau=0.85$)', + color='#1565C0', alpha=0.85) +ax11b.set_xlabel('Number of users $U$') +ax11b.set_ylabel('UWCA-SE gain over OFDMA (dB)\nat SER = 0.30') +ax11b.set_title('(b) UWCA-SE SNR gain vs $U$', fontsize=10, pad=6) +ax11b.set_xticks(_u_vals_plot) +ax11b.legend(fontsize=9) +ax11b.grid(True, axis='y', alpha=0.3) +ax11b.set_xlim(0.5, 4.5) + +fig11.tight_layout() +fig11.savefig(f'{OUT_DIR}/fig11_fair_comparison.png', dpi=150, + bbox_inches='tight', facecolor='#F8F9FA') +fig11.savefig(f'{OUT_DIR}/fig11_fair_comparison.pdf', + bbox_inches='tight', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig11_fair_comparison.png/.pdf") + + +# ══════════════════════════════════════════════════════════════════════════════ +# FIGURE 12 — U-variation: β=0.9 / 0.5 / 0.1 comparison (high-precision MC) +# ══════════════════════════════════════════════════════════════════════════════ +# OFDMA : fixed 16-dim allocation (U=4 result), ONE solid line per panel. +# UWCA-SE: U∈{1,2,4} — line + small markers, different colors. +# Legend : inside each panel, lower-left. +# ══════════════════════════════════════════════════════════════════════════════ + +_MKR_EVERY12 = max(1, len(_SNR_F12) // 8) # ~every 2-3 dB + +fig12, (ax12a, ax12b, ax12c) = plt.subplots(1, 3, figsize=(18, 6)) +fig12.patch.set_facecolor('#F8F9FA') +_style_ax(ax12a); _style_ax(ax12b); _style_ax(ax12c) + +def _draw_panel12(ax, u_results, beta_label, panel_tag): + # OFDMA: single solid line (U=4, 16-dim fixed allocation) + ax.semilogy(_SNR_F12, u_results[4]['OFDMA']['ser'], + '-', lw=2.2, color=_OFDMA_CLR12, zorder=2) + # UWCA-SE: line + small markers per U value + for U_val in _U_LIST_12: + ax.semilogy(_SNR_F12, u_results[U_val]['UWCA']['ser'], + '-', lw=1.5, color=_UWCA_CLRS12[U_val], + marker=_UWCA_MKRS12[U_val], markevery=_MKR_EVERY12, + ms=4, zorder=3) + ax.set_xlabel('SNR (dB)', fontsize=17) + if panel_tag == 'a': + ax.set_ylabel('SER', fontsize=17) + ax.tick_params(labelsize=16) + ax.grid(True, alpha=0.3); ax.set_xlim(0, 20) + _handles = [ + _L2D([0],[0], color=_OFDMA_CLR12, lw=2.2, ls='-', + label='OFDMA'), + _L2D([0],[0], color=_UWCA_CLRS12[1], lw=1.5, ls='-', + marker=_UWCA_MKRS12[1], ms=4, label='UWCA ($U=1$)'), + _L2D([0],[0], color=_UWCA_CLRS12[2], lw=1.5, ls='-', + marker=_UWCA_MKRS12[2], ms=4, label='UWCA ($U=2$)'), + _L2D([0],[0], color=_UWCA_CLRS12[4], lw=1.5, ls='-', + marker=_UWCA_MKRS12[4], ms=4, label='UWCA ($U=4$)'), + ] + ax.legend(handles=_handles, loc='lower left', fontsize=14, framealpha=0.9) + # Label + β value shown only at the bottom + ax.text(0.5, -0.20, f'({panel_tag}) $\\beta = {beta_label}$', + transform=ax.transAxes, ha='center', va='top', + fontsize=17, fontweight='bold') + +_draw_panel12(ax12a, u_var_f12_09, '0.9', 'a') +_draw_panel12(ax12b, u_var_f12_05, '0.5', 'b') +_draw_panel12(ax12c, u_var_f12_01, '0.1', 'c') + +fig12.tight_layout() +fig12.savefig(f'{OUT_DIR}/fig12_high_low_u_variation.png', dpi=150, + bbox_inches='tight', facecolor='#F8F9FA') +fig12.savefig(f'{OUT_DIR}/fig12_high_low_u_variation.pdf', + bbox_inches='tight', facecolor='#F8F9FA') +plt.close() +print(f"Saved: {OUT_DIR}/fig12_high_low_u_variation.png/.pdf") + + +# ══════════════════════════════════════════════════════════════════════════════ +# 9. Numerical summary +# ══════════════════════════════════════════════════════════════════════════════ +print("\n" + "=" * 76) +print("NUMERICAL SUMMARY") +print("=" * 76) + +for sk in ['HIGH', 'LOW', 'MIX']: + cfg = SCENARIOS[sk] + beta_mat = results[sk]['_beta_mat'] + bu = np.array(cfg['beta_u']) + rho_off_m = np.abs(results[sk]['_rho_m'][mask]).mean() + print(f"\n[{sk}] beta_u = {bu} | beta_uv (off-diag mean) = " + f"{beta_mat[mask].mean():.3f}") + print(f" {'Method':<18} {'SER@4dB':>8} {'SER@10dB':>9} {'SER@16dB':>9} " + f"{'|rho_off|':>10}") + print(" " + "-" * 60) + for m in ['OFDMA', 'MAML+Attn']: + rho_str = f"{rho_off_m:>10.4f}" if m == 'MAML+Attn' else " —" + print(f" {m:<18} " + f"{results[sk][m]['ser'][IDX4]:>8.4f} " + f"{results[sk][m]['ser'][IDX10]:>9.4f} " + f"{results[sk][m]['ser'][IDX16]:>9.4f}" + f"{rho_str}") + +print("\n" + "-" * 76) +print("Beta sweep (SER gain vs OFDMA @ 10 dB):") +print(f" {'beta_uv':>8} {'MAML gain':>11}") +for bv, gm in zip(BETAS2, beta_sweep['gain_maml']): + print(f" {bv:>8.3f} {gm:>+11.4f}") + +print("\n" + "-" * 76) +print("MAML Ablation (MIX scenario, SNR = 10 dB):") +print(f" {'S':>4} {'SER':>8}") +for Sv, sv in zip(ablation['S_values'], ablation['ser']): + print(f" {Sv:>4} {sv:>8.4f}") +print(f" {'inf':>4} {ablation['ser_ideal']:>8.4f} (fully adapted)") + +print("\n" + "=" * 76) +print("U-VARIATION SUMMARY (HIGH scenario, beta=0.95, tau={:.2f})".format(TAU)) +print("=" * 76) +_idx10 = int(np.argmin(np.abs(SNR_DB - 10))) +_idx20 = int(np.argmin(np.abs(SNR_DB - 20))) +print(f" {'U':>3} {'DPU':>5} {'OFDMA@10dB':>12} {'UWCA@10dB':>12} " + f"{'OFDMA@20dB':>12} {'UWCA@20dB':>12} {'MI gain':>10}") +for _U_val in [1, 2, 3, 4]: + _ru = u_var_results[_U_val] + _mb = mi_bounds[_U_val] + _mi20 = _mb['I_uwca'][-1] / max(_mb['I_ofdma'][-1], 1e-9) + print(f" {_U_val:>3} {D//_U_val:>5} " + f"{_ru['OFDMA']['ser'][_idx10]:>12.4f} " + f"{_ru['UWCA']['ser'][_idx10]:>12.4f} " + f"{_ru['OFDMA']['ser'][_idx20]:>12.4f} " + f"{_ru['UWCA']['ser'][_idx20]:>12.4f} " + f"{_mi20:>10.2f}x") + +print("\n" + "-" * 76) +print("MI BOUNDS @ SNR = 10 / 20 dB (analytical, beta=0.95):") +for _U_val in [1, 2, 3, 4]: + _mb = mi_bounds[_U_val] + _i10 = int(np.argmin(np.abs(MI_SNRS - 10))) + _i20 = int(np.argmin(np.abs(MI_SNRS - 20))) + print(f" U={_U_val}: OFDMA-SE={_mb['I_ofdma'][_i10]:.2f}/{_mb['I_ofdma'][_i20]:.2f} bits, " + f"UWCA-SE={_mb['I_uwca'][_i10]:.2f}/{_mb['I_uwca'][_i20]:.2f} bits " + f"(ratio {_mb['I_uwca'][_i20]/max(_mb['I_ofdma'][_i20],1e-9):.2f}x @ 20dB, " + f"low-SNR peak={_mb['ratio_low_snr']:.2f}x, high-SNR limit=1.00x)") + +print("\n" + "=" * 76) +print(f"All figures saved to {OUT_DIR}/") +print(" fig1_ser_high_low_mix.png — SER vs SNR: HIGH / LOW / MIX") +print(" fig3_per_user_ser.png — Per-user SER: MIX scenario") +print(" fig4_beta_sweep.png — SER gain vs beta_uv (Prop. 1 validation)") +print(" fig6_attn_heatmaps.png — Attention matrices: HIGH / LOW / MIX") +print(" fig8_ablation.png — MAML inner-loop steps S ablation") +print(" fig9_u_variation_ser.png — SER vs SNR: U-user scaling (U=1,2,3,4)") +print(" fig10_mutual_information.png — MI bounds & gain ratio vs SNR") +print(" fig12_high_low_u_variation.png — HIGH vs LOW: statistical gain + cross-attn role") +print("=" * 76) diff --git a/legacy/revision_ablation.py b/legacy/revision_ablation.py new file mode 100755 index 0000000..0f1b8de --- /dev/null +++ b/legacy/revision_ablation.py @@ -0,0 +1,124 @@ +""" +Ablation / sensitivity studies for the TWC revision. + Part 1 (torch): H (heads), K (number of SNR meta-tasks), S (inner steps) + sensitivity -> SER@10dB, cos@10dB, |rho_off|@10dB on HIGH. + Part 2 (numpy): refined scalability -> mean cosine + top-k retention vs U. +""" +import types +import numpy as np +import torch +import maml_semantic as M + +torch.manual_seed(0) + +class _Sim: # minimal stand-ins (avoid importing the heavy sim module) + BATCH = 64 + @staticmethod + def _norm(E): + return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8) + @staticmethod + def cos_sim(Eh, Eg): + return (Eh * Eg).sum(-1) +sim = _Sim() + +def make_cfg(**kw): + base = dict(d=64, U=4, H=4, tau=0.45, lam=0.1, snr_min=0.0, snr_max=20.0, + snr_step=2.0, inner_lr=0.01, inner_steps=5, outer_lr=1e-3, + meta_epochs=70, joint_epochs=70, batch=64, n_mc=60, seed=42, + scenario='HIGH', decoder_only=True) + base.update(kw) + return types.SimpleNamespace(**base) + +def train_eval(cfg): + device = 'cpu' + rng = np.random.default_rng(cfg.seed) + scen = M.SCENARIO_CONFIGS[cfg.scenario] + model = M.SemanticCommSystem(cfg.d, cfg.U, cfg.H, decoder_only=cfg.decoder_only).to(device) + tr = M.MAMLTrainer(model, cfg, device, rng, scen) + tr.train() + res = M.evaluate_model(model, cfg, device, rng, "rayleigh", scen) + snr = np.arange(cfg.snr_min, cfg.snr_max + 1e-6, cfg.snr_step) + i10 = int(np.argmin(np.abs(snr - 10))) + rho = res['rho'][i10] + mask = ~np.eye(cfg.U, dtype=bool) + return res['ser'][i10], res['cos'][i10], float(np.abs(rho[mask]).mean()), len(snr) + +def part1(): + print("\n=== ABLATION (HIGH scenario, decoder-only, SNR=10 dB) ===") + print("\n-- Attention heads H (d=64) --") + for H in [1, 2, 4, 8]: + ser, cos, rho, _ = train_eval(make_cfg(H=H)) + print(f" H={H}: dk={64//H:2d} SER={ser:.3f} cos={cos:.3f} |rho_off|={rho:.3f}") + print("\n-- Number of SNR meta-tasks K (via snr_step) --") + for step in [20.0, 10.0, 4.0, 2.0, 1.0]: + ser, cos, rho, K = train_eval(make_cfg(snr_step=step)) + print(f" K={K:2d} (step={step:>4}): SER={ser:.3f} cos={cos:.3f} |rho_off|={rho:.3f}") + print("\n-- Inner-loop steps S --") + for S in [1, 3, 5, 10]: + ser, cos, rho, _ = train_eval(make_cfg(inner_steps=S)) + print(f" S={S:2d}: SER={ser:.3f} cos={cos:.3f} |rho_off|={rho:.3f}") + +# ---- Part 2: refined scalability (cosine + top-k retention) ---- +RNG = np.random.default_rng(11) + +def gen_clustered(n, U, D, g, beta): + nc = U // g + scenes = [RNG.standard_normal(D) for _ in range(nc)] + scenes = [s / np.linalg.norm(s) for s in scenes] + embs = [] + for u in range(U): + s = scenes[u // g] + priv = RNG.standard_normal((n, D)); priv /= np.linalg.norm(priv, axis=-1, keepdims=True) + 1e-8 + e = np.sqrt(1 - beta**2) * priv + beta * s[None, :] + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + embs.append(e) + return np.stack(embs, 1) + +def masks_for(U, D): + dpu = D // U; Mk = np.zeros((U, D)) + for u in range(U): + Mk[u, u*dpu:(u+1)*dpu] = 1.0 + return Mk + +def se_chan(E, snr_db, Mk): + n, U, D = E.shape + Ytx = (E * Mk[None]).sum(1) + h = np.sqrt(RNG.standard_normal((n, U, 1))**2 + RNG.standard_normal((n, U, 1))**2) * np.sqrt(0.5) + nstd = np.sqrt(float(np.mean(Ytx**2)) / (10**(snr_db/10))) + return h * Ytx[:, None, :] + RNG.standard_normal((n, U, D)) * nstd + +def attn(Yrx, Mk, bm, topk=None): + n, U, D = Yrx.shape + R = Yrx[:, :, None, :] * Mk[None, None] + a = bm.copy(); np.fill_diagonal(a, 1.0) + if topk is not None and topk < U: + for u in range(U): + order = np.argsort(-a[u]); keep = set(order[:topk]) | {u} + for v in range(U): + if v not in keep: a[u, v] = 0.0 + a /= a.sum(1, keepdims=True) + 1e-8 + ctx = np.einsum('ui,buid->bud', a, R) + return np.stack([sim._norm(ctx[:, u, :]) for u in range(U)], 1) + +def part2(): + print("\n=== SCALABILITY: cosine + top-k retention (g=4, beta=0.65, SNR=20 dB) ===") + g, beta, snr = 4, 0.65, 20.0 + for U in [4, 8, 16, 32]: + D = 16 * U; Mk = masks_for(U, D) + bm = np.zeros((U, U)) + for i in range(U): + for j in range(U): + if i // g == j // g: bm[i, j] = beta*beta + nmc = 150; cf = ct = 0.0 + for _ in range(nmc): + E = gen_clustered(sim.BATCH, U, D, g, beta) + cf += sim.cos_sim(attn(se_chan(E, snr, Mk), Mk, bm), E).mean() + ct += sim.cos_sim(attn(se_chan(E, snr, Mk), Mk, bm, topk=g), E).mean() + cf /= nmc; ct /= nmc + print(f" U={U:3d} cos_full={cf:.3f} cos_topk(k={g})={ct:.3f} " + f"retention={100*ct/cf:5.1f}% ops full={U*U} topk={U*g} ({U//g}x)") + +if __name__ == '__main__': + part1() + part2() + print("\nDONE.") diff --git a/legacy/revision_betasweep.py b/legacy/revision_betasweep.py new file mode 100755 index 0000000..4f61142 --- /dev/null +++ b/legacy/revision_betasweep.py @@ -0,0 +1,39 @@ +"""Fig. 3 (SER gain vs relevance coefficient) regenerated with the SAME box +aspect (0.8) as the Fig. 2 panels, from results/data/beta_sweep.csv.""" +import csv, numpy as np +import matplotlib; matplotlib.use('Agg') +import matplotlib.pyplot as plt + +rows = list(csv.DictReader(open('results/data/beta_sweep.csv'))) +SNRS = [0.0, 5.0, 10.0] +STY = {0.0: ('#C62828', 's--', 'SNR = 0 dB'), + 5.0: ('#E65100', '^-.', 'SNR = 5 dB'), + 10.0: ('#1565C0', 'o-', 'SNR = 10 dB')} +FILL = {0.0: '#C62828', 5.0: '#E65100', 10.0: '#1565C0'} + +data = {s: {'b': [], 'g': []} for s in SNRS} +for r in rows: + s = float(r['snr_label']) + if s in data: + data[s]['b'].append(float(r['beta_sq'])); data[s]['g'].append(float(r['gain_maml'])) +for s in SNRS: + o = np.argsort(data[s]['b']) + data[s]['b'] = np.array(data[s]['b'])[o]; data[s]['g'] = np.array(data[s]['g'])[o] + +fig, ax = plt.subplots(figsize=(5.2, 4.6)) +for s in SNRS: + c, mk, lbl = STY[s] + ax.plot(data[s]['b'], data[s]['g'], mk, lw=2.0, color=c, label=lbl, markersize=5) + ax.fill_between(data[s]['b'], 0, data[s]['g'], alpha=0.07, color=FILL[s]) +ax.axhline(0, color='gray', lw=0.8, ls=':') +ax.set_xlabel(r'Semantic relevance coefficient $\beta_{u,v}=\beta_u\cdot\beta_v$', fontsize=13) +ax.set_ylabel('SER gain over OFDMA', fontsize=13) +ax.tick_params(labelsize=12) +ax.legend(loc='upper left', fontsize=12) +ax.grid(True, alpha=0.3) +ax.set_xlim(-0.01, 0.82) +ax.set_box_aspect(0.8) # match the Fig. 2 panel box aspect +fig.tight_layout() +fig.savefig('results/fig4_beta_sweep.pdf', bbox_inches='tight') +fig.savefig('results/fig4_beta_sweep.png', dpi=150, bbox_inches='tight') +print('saved results/fig4_beta_sweep.pdf (box_aspect=0.8)') diff --git a/legacy/revision_dsweep.py b/legacy/revision_dsweep.py new file mode 100755 index 0000000..6a28d20 --- /dev/null +++ b/legacy/revision_dsweep.py @@ -0,0 +1,20 @@ +import types, numpy as np, torch +import maml_semantic as M +torch.manual_seed(0) +def cfg(**k): + b=dict(d=64,U=4,H=4,tau=0.45,lam=0.1,snr_min=0.0,snr_max=20.0,snr_step=2.0, + inner_lr=0.01,inner_steps=5,outer_lr=1e-3,meta_epochs=70,joint_epochs=70, + batch=64,n_mc=60,seed=42,scenario='HIGH',decoder_only=True); b.update(k) + return types.SimpleNamespace(**b) +def run(d): + c=cfg(d=d); dev='cpu'; rng=np.random.default_rng(42); scen=M.SCENARIO_CONFIGS['HIGH'] + m=M.SemanticCommSystem(c.d,c.U,c.H,decoder_only=True).to(dev) + M.MAMLTrainer(m,c,dev,rng,scen).train() + res=M.evaluate_model(m,c,dev,rng,'rayleigh',scen) + snr=np.arange(0,20.0001,2); i=int(np.argmin(np.abs(snr-10))) + rho=res['rho'][i]; mask=~np.eye(c.U,dtype=bool) + return res['ser'][i],res['cos'][i],float(np.abs(rho[mask]).mean()) +print("=== d sweep (HIGH, SNR=10dB) ===") +for d in [32,64,128]: + s,co,r=run(d); print(f"d={d:3d}: SER={s:.3f} cos={co:.3f} |rho_off|={r:.3f}") +print("DONE") diff --git a/legacy/revision_e2e.py b/legacy/revision_e2e.py new file mode 100755 index 0000000..6c8a9cb --- /dev/null +++ b/legacy/revision_e2e.py @@ -0,0 +1,31 @@ +"""End-to-end (learnable encoder) vs decoder-only MAML, HIGH scenario. +Verifies Reviewer-1 Comment-3 claim that cross-attention stays stable and +effective when the encoder is also learned.""" +import types, numpy as np, torch +import maml_semantic as M +torch.manual_seed(0) + +def cfg(**k): + b = dict(d=64, U=4, H=4, tau=0.45, lam=0.1, snr_min=0.0, snr_max=20.0, + snr_step=2.0, inner_lr=0.01, inner_steps=5, outer_lr=1e-3, + meta_epochs=120, joint_epochs=120, batch=64, n_mc=80, seed=42, + scenario='HIGH', decoder_only=True) + b.update(k); return types.SimpleNamespace(**b) + +def run(decoder_only): + c = cfg(decoder_only=decoder_only); dev='cpu'; rng=np.random.default_rng(c.seed) + scen=M.SCENARIO_CONFIGS[c.scenario] + mdl=M.SemanticCommSystem(c.d,c.U,c.H,decoder_only=decoder_only).to(dev) + M.MAMLTrainer(mdl,c,dev,rng,scen).train() + res=M.evaluate_model(mdl,c,dev,rng,"rayleigh",scen) + snr=np.arange(0,20.0001,2); + def at(s): return res['ser'][int(np.argmin(np.abs(snr-s)))], res['cos'][int(np.argmin(np.abs(snr-s)))] + i10=int(np.argmin(np.abs(snr-10))); rho=res['rho'][i10]; mask=~np.eye(c.U,dtype=bool) + return at(4), at(10), at(16), float(np.abs(rho[mask]).mean()) + +print("=== END-TO-END (learnable encoder) vs DECODER-ONLY, HIGH ===") +for tag, do in [("decoder-only (frozen enc)", True), ("end-to-end (learnable enc)", False)]: + (s4,c4),(s10,c10),(s16,c16),rho = run(do) + print(f" {tag:30s} SER@4/10/16 = {s4:.3f}/{s10:.3f}/{s16:.3f} " + f"cos@10={c10:.3f} |rho_off|@10={rho:.3f}") +print("DONE.") diff --git a/legacy/revision_experiments.py b/legacy/revision_experiments.py new file mode 100755 index 0000000..7a70aaa --- /dev/null +++ b/legacy/revision_experiments.py @@ -0,0 +1,197 @@ +""" +Auxiliary experiments for the TWC revision (main_FFF.tex). +Reuses the validated simulation primitives in semantic_correlation_sim.py to +produce REAL numbers for the new reviewer-requested studies: + + A. Threshold sensitivity (tau = 0.30..0.50) + mean cosine similarity [R1.2, R3.2] + B. Residual phase-error robustness [R1.1, R2.3] + C. User scaling U in {4,8,16,32} : full vs. sparse top-k attention [R1.6, R3.4] + D. DL semantic baseline: Joint+Attn calibrated at a single nominal SNR [R1.5,R2.5,R3.5] + vs. MAML SNR-adaptive shrinkage + +All results are printed as LaTeX-ready rows. +""" +import numpy as np +import semantic_correlation_sim as sim + +RNG = np.random.default_rng(2026) + +# ---------------------------------------------------------------------------- +# A. Threshold sensitivity + mean cosine similarity (HIGH/LOW/MIX, SNR=10 dB) +# ---------------------------------------------------------------------------- +def collect_cos(scenario_key, snr=10.0, n_mc=600): + cfg = sim.SCENARIOS[scenario_key] + beta_mat = sim.compute_beta_matrix(cfg) + out = {'OFDMA': [], 'NOMA-SIC': [], 'UWCA': []} + for _ in range(n_mc): + Egt = sim.gen_embeddings(sim.BATCH, scenario_key) + Y = sim.shared_embedding_channel(Egt, snr) + Eh, _ = sim.ofdma_se_decoder(Y) + out['OFDMA'].append(sim.cos_sim(Eh, Egt).ravel()) + y, h = sim.noma_ul_channel(Egt, snr) + Eh = sim.noma_sic_decoder(y, h) + out['NOMA-SIC'].append(sim.cos_sim(Eh, Egt).ravel()) + Y = sim.shared_embedding_channel(Egt, snr) + Eh, _ = sim.maml_attention_se_decoder(Y, snr, beta_mat) + out['UWCA'].append(sim.cos_sim(Eh, Egt).ravel()) + return {k: np.concatenate(v) for k, v in out.items()} + +def exp_A(): + print("\n=== EXP A: threshold sensitivity + mean cosine (SNR=10 dB) ===") + taus = [0.30, 0.35, 0.40, 0.45, 0.50] + for scen in ['HIGH', 'LOW', 'MIX']: + cos = collect_cos(scen) + print(f"\n[{scen}]") + for m in ['OFDMA', 'NOMA-SIC', 'UWCA']: + c = cos[m] + sers = [f"{(c < t).mean():.3f}" for t in taus] + print(f" {m:9s} meancos={c.mean():.3f} SER@tau[{','.join(map(str,taus))}] = {sers}") + +# ---------------------------------------------------------------------------- +# B. Residual phase-error robustness +# After imperfect pilot-based compensation, residual phase Dphi ~ N(0,sig^2); +# recovered in-phase component scales by cos(Dphi) (quadrature energy lost). +# ---------------------------------------------------------------------------- +def se_channel_phase(E, snr_db, sigma_phi_deg): + n, U, D = E.shape + X = E * sim.MASKS[None, :, :] + Ytx = X.sum(axis=1) + h = (np.sqrt(RNG.standard_normal((n, U, 1))**2 + RNG.standard_normal((n, U, 1))**2) + * np.sqrt(0.5)) + sig_power = float(np.mean(Ytx**2)) + noise_std = np.sqrt(sig_power / (10**(snr_db/10))) + phi = np.deg2rad(sigma_phi_deg) * RNG.standard_normal((n, U, 1)) + Yrx = h * np.cos(phi) * Ytx[:, None, :] + RNG.standard_normal((n, U, D)) * noise_std + return Yrx + +def exp_B(): + print("\n=== EXP B: residual phase-error robustness (HIGH scenario) ===") + cfg = sim.SCENARIOS['HIGH']; beta_mat = sim.compute_beta_matrix(cfg) + for snr in [10.0, 20.0]: + row = [] + for sig in [0, 5, 10, 15, 20]: + acc = 0.0; n_mc = 400 + for _ in range(n_mc): + Egt = sim.gen_embeddings(sim.BATCH, 'HIGH') + Y = se_channel_phase(Egt, snr, sig) + Eh, _ = sim.maml_attention_se_decoder(Y, snr, beta_mat) + acc += sim.ser_total(Eh, Egt) + row.append(f"{acc/n_mc:.3f}") + print(f" SNR={snr:4.0f}dB UWCA-SER vs sigma_phi[0,5,10,15,20 deg] = {row}") + +# ---------------------------------------------------------------------------- +# C. User scaling + sparse top-k attention (clustered relevance) +# U users in clusters of size g sharing a scene; cross-cluster beta=0. +# full attention: O(U^2) ; top-k (k=g): O(U*k). +# ---------------------------------------------------------------------------- +def gen_clustered(n, U, D, g, beta): + n_clusters = U // g + scenes = [] + for _ in range(n_clusters): + s = RNG.standard_normal(D); scenes.append(s / np.linalg.norm(s)) + embs = [] + for u in range(U): + s = scenes[u // g] + priv = RNG.standard_normal((n, D)) + priv /= np.linalg.norm(priv, axis=-1, keepdims=True) + 1e-8 + e = np.sqrt(1 - beta**2) * priv + beta * s[None, :] + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + embs.append(e) + return np.stack(embs, axis=1) + +def masks_for(U, D): + dpu = D // U + M = np.zeros((U, D)) + for u in range(U): + M[u, u*dpu:(u+1)*dpu] = 1.0 + return M + +def se_channel_generic(E, snr_db, M): + n, U, D = E.shape + X = E * M[None, :, :] + Ytx = X.sum(axis=1) + h = (np.sqrt(RNG.standard_normal((n, U, 1))**2 + RNG.standard_normal((n, U, 1))**2) + * np.sqrt(0.5)) + noise_std = np.sqrt(float(np.mean(Ytx**2)) / (10**(snr_db/10))) + return h * Ytx[:, None, :] + RNG.standard_normal((n, U, D)) * noise_std + +def attn_decode(Yrx, M, beta_mat, topk=None): + n, U, D = Yrx.shape + R = Yrx[:, :, None, :] * M[None, None, :, :] # (n,U,U,D) + alpha = beta_mat.copy(); np.fill_diagonal(alpha, 1.0) + if topk is not None and topk < U: + # keep self + top-(k-1) strongest cross weights per row + for u in range(U): + order = np.argsort(-alpha[u]) + keep = set(order[:topk].tolist()) | {u} + for v in range(U): + if v not in keep: + alpha[u, v] = 0.0 + alpha /= alpha.sum(1, keepdims=True) + 1e-8 + ctx = np.einsum('ui,buid->bud', alpha, R) + Eh = np.stack([sim._norm(ctx[:, u, :]) for u in range(U)], axis=1) + return Eh + +def exp_C(): + print("\n=== EXP C: user scaling + sparse top-k attention (g=4, beta=0.65, SNR=10 dB) ===") + g = 4; beta = 0.65; snr = 10.0 + for U in [4, 8, 16, 32]: + D = 16 * U # keep 16 dims/user + M = masks_for(U, D) + bm = np.zeros((U, U)) + for i in range(U): + for j in range(U): + if i // g == j // g: + bm[i, j] = beta * beta + n_mc = 200 + ser_full = ser_topk = 0.0 + for _ in range(n_mc): + Egt = gen_clustered(sim.BATCH, U, D, g, beta) + Y = se_channel_generic(Egt, snr, M) + ser_full += float((sim.cos_sim(attn_decode(Y, M, bm), Egt) < 0.45).mean()) + Y2 = se_channel_generic(Egt, snr, M) + ser_topk += float((sim.cos_sim(attn_decode(Y2, M, bm, topk=g), Egt) < 0.45).mean()) + ops_full = U * U + ops_topk = U * g + print(f" U={U:3d} SER_full={ser_full/n_mc:.3f} SER_topk(k={g})={ser_topk/n_mc:.3f}" + f" attn_ops: full={ops_full} topk={ops_topk} reduction={ops_full/ops_topk:.1f}x") + +# ---------------------------------------------------------------------------- +# D. DL semantic baseline: Joint+Attn calibrated at single nominal SNR (10 dB) +# vs. MAML SNR-adaptive shrinkage. Wiener-type shrinkage s = g/(g+1) applied +# to the aggregated cross-attention context; MAML adapts s to the test SNR, +# the non-meta Joint baseline is frozen at the training SNR. +# ---------------------------------------------------------------------------- +def attn_decode_shrink(Yrx, M, beta_mat, shrink): + n, U, D = Yrx.shape + R = Yrx[:, :, None, :] * M[None, None, :, :] + alpha = beta_mat.copy(); np.fill_diagonal(alpha, 0.0) + alpha /= (alpha.sum(1, keepdims=True) + 1e-8) + cross = np.einsum('ui,buid->bud', alpha, R) # cross context + own = np.einsum('buud->bud', R.transpose(0,1,2,3)) # placeholder + own = Yrx * M[None, :, :] # own subspace skip + ctx = shrink * cross + own + Eh = np.stack([sim._norm(ctx[:, u, :]) for u in range(U)], axis=1) + return Eh + +def exp_D(): + print("\n=== EXP D: DL baseline (Joint+Attn fixed 10 dB) vs MAML adaptive ===") + cfg = sim.SCENARIOS['HIGH']; bm = sim.compute_beta_matrix(cfg) + g0 = 10**(10/10); shrink_fixed = g0/(g0+1) # calibrated at 10 dB + for snr in [0, 5, 10, 15, 20]: + g = 10**(snr/10); shrink_adapt = g/(g+1) + n_mc = 300; ser_fixed = ser_adapt = 0.0 + for _ in range(n_mc): + Egt = sim.gen_embeddings(sim.BATCH, 'HIGH') + Y = sim.shared_embedding_channel(Egt, snr) + ser_fixed += float((sim.cos_sim(attn_decode_shrink(Y, sim.MASKS, bm, shrink_fixed), Egt) < 0.45).mean()) + Y2 = sim.shared_embedding_channel(Egt, snr) + ser_adapt += float((sim.cos_sim(attn_decode_shrink(Y2, sim.MASKS, bm, shrink_adapt), Egt) < 0.45).mean()) + print(f" SNR={snr:3d}dB Joint+Attn(fixed10dB)={ser_fixed/n_mc:.3f} MAML-UWCA(adaptive)={ser_adapt/n_mc:.3f}") + +if __name__ == '__main__': + exp_A() + exp_B() + exp_C() + exp_D() + print("\nDONE.") diff --git a/legacy/revision_realdata2.py b/legacy/revision_realdata2.py new file mode 100755 index 0000000..900b05b --- /dev/null +++ b/legacy/revision_realdata2.py @@ -0,0 +1,104 @@ +""" +Real-data validation v2 (self-contained; no heavy import). +sklearn digits (8x8=64-dim REAL images) -> mean-centered, unit-normalized +embeddings. Mean removal decorrelates the shared ink/DC structure so the LOW +scenario attains genuinely low inter-user relevance. + +Adds the downstream task-accuracy metric (nearest class-prototype) alongside +SER and mean cosine, and reports the empirical beta_uv per scenario. +""" +import numpy as np +from sklearn.datasets import load_digits + +RNG = np.random.default_rng(7) +U, D = 4, 64 +DPU = D // U +MASKS = np.zeros((U, D)) +for u in range(U): + MASKS[u, u*DPU:(u+1)*DPU] = 1.0 +NOMA_POWER = np.array([0.40, 0.30, 0.20, 0.10]) +TAU = 0.45 + +X, y = load_digits(return_X_y=True) +X = X.astype(np.float64) +X = X - X.mean(0, keepdims=True) # remove shared DC structure +X = X / (np.linalg.norm(X, axis=1, keepdims=True) + 1e-8) +by_class = {c: X[y == c] for c in range(10)} +# class prototypes (gallery) for the downstream nearest-prototype classifier +PROTO = np.stack([by_class[c].mean(0) for c in range(10)]) +PROTO = PROTO / (np.linalg.norm(PROTO, axis=1, keepdims=True) + 1e-8) + +def _norm(E): return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8) +def cos_sim(Eh, Eg): return (Eh * Eg).sum(-1) + +def se_channel(E, snr_db): + n = E.shape[0] + X_ = E * MASKS[None]; Ytx = X_.sum(1) + h = np.sqrt(RNG.standard_normal((n, U, 1))**2 + RNG.standard_normal((n, U, 1))**2) * np.sqrt(0.5) + nstd = np.sqrt(float(np.mean(Ytx**2)) / (10**(snr_db/10))) + return h * Ytx[:, None, :] + RNG.standard_normal((n, U, D)) * nstd + +def ofdma_decode(Yrx): + return np.stack([_norm(Yrx[:, u, :] * MASKS[u]) for u in range(U)], 1) + +def noma_channel(E, snr_db): + n = E.shape[0] + h = np.sqrt(RNG.standard_normal((n, U, 1))**2 + RNG.standard_normal((n, U, 1))**2) * np.sqrt(0.5) + w = E * np.sqrt(NOMA_POWER)[None, :, None] * h + yv = w.sum(1) + nstd = np.sqrt(float(np.mean(yv**2)) / (10**(snr_db/10))) + return yv + RNG.standard_normal((n, D)) * nstd, h + +def noma_sic(yv, h): + n = yv.shape[0]; Eh = np.zeros((n, U, D)); res = yv.copy() + for u in range(U): + z = res / (h[:, u, :] + 1e-8); Eh[:, u, :] = _norm(z) + res -= h[:, u, :] * np.sqrt(NOMA_POWER[u]) * Eh[:, u, :] + return Eh + +def uwca_decode(Yrx, beta_mat): + R = Yrx[:, :, None, :] * MASKS[None, None] + a = beta_mat.copy(); np.fill_diagonal(a, 1.0); a /= a.sum(1, keepdims=True) + 1e-8 + ctx = np.einsum('ui,buid->bud', a, R) + return np.stack([_norm(ctx[:, u, :]) for u in range(U)], 1) + +SCEN = {'HIGH': [3, 3, 3, 3], 'LOW': [0, 1, 7, 4], 'MIX': [3, 3, 8, 1]} + +def sample_users(n, ca): + return np.stack([by_class[ca[u]][RNG.integers(0, len(by_class[ca[u]]), n)] for u in range(U)], 1) + +def empirical_beta(ca, n=4000): + E = sample_users(n, ca) + B = np.einsum('nud,nvd->uv', E, E) / n + return B + +def downstream_acc(Eh, labels): + # labels: (U,) true class per user; Eh: (n,U,D) + sims = np.einsum('nud,cd->nuc', _norm(Eh), PROTO) # (n,U,10) + pred = sims.argmax(-1) # (n,U) + return (pred == np.array(labels)[None, :]).mean() + +def run(scen, snr, n_mc=400): + ca = SCEN[scen]; bm = empirical_beta(ca); bmc = bm.copy(); np.fill_diagonal(bmc, 0.0); bmc = np.clip(bmc, 0, None) + M = {'OFDMA': [0,0,0], 'NOMA-SIC': [0,0,0], 'UWCA': [0,0,0]} # ser, cos, acc + for _ in range(n_mc): + E = sample_users(64, ca) + Y = se_channel(E, snr); Eh = ofdma_decode(Y) + M['OFDMA'][0]+=(cos_sim(Eh,E)bud', a, R) + return np.stack([_norm(ctx[:, u, :]) for u in range(U)], 1) + +SCEN = {'HIGH': [3, 3, 3, 3], 'LOW': [0, 1, 7, 4], 'MIX': [3, 3, 8, 1]} +def sample(n, ca): return np.stack([by_class[ca[u]][RNG.integers(0, len(by_class[ca[u]]), n)] for u in range(U)], 1) +def emp_beta(ca, n=4000): + E = sample(n, ca); return np.einsum('nud,nvd->uv', E, E)/n +def acc(Eh, ca): + pred = np.einsum('nud,cd->nuc', _norm(Eh), PROTO).argmax(-1) + return (pred == np.array(ca)[None, :]).mean() + +SNR = np.arange(0, 21, 2) +trained = json.load(open('results/realdata_train.json')) # trained UWCA w/ and w/o MAML + +# --- recompute analytical / baseline accuracy (shared MC) --- +ana = {s: {m: [] for m in ['OFDMA', 'SFDMA', 'NOMA-SIC', 'UWCA (analytical)']} for s in SCEN} +for s, ca in SCEN.items(): + bm = emp_beta(ca); bmc = bm.copy(); np.fill_diagonal(bmc, 0.0); bmc = np.clip(bmc, 0, None) + for snr in SNR: + ao = asf = an = au = 0.0; nmc = 200 + for _ in range(nmc): + E = sample(64, ca) + ao += acc(ofdma(se_channel(E, snr)), ca) + asf += acc(sfdma(E, snr), ca) + yv, h = noma_ch(E, snr); an += acc(noma_sic(yv, h), ca) + au += acc(uwca_oracle(se_channel(E, snr), bmc), ca) + ana[s]['OFDMA'].append(ao/nmc); ana[s]['SFDMA'].append(asf/nmc) + ana[s]['NOMA-SIC'].append(an/nmc); ana[s]['UWCA (analytical)'].append(au/nmc) + +# --- plot: downstream accuracy, 1 row x 3 cols --- +COL = {'OFDMA': '#546E7A', 'SFDMA': '#9C27B0', 'NOMA-SIC': '#E65100', + 'UWCA (analytical)': '#1565C0', 'UWCA w/o MAML': '#2E7D32', 'UWCA w/ MAML': '#1565C0'} +fig, ax = plt.subplots(1, 3, figsize=(11, 3.4)) +betas = {s: emp_beta(SCEN[s])[~np.eye(U, dtype=bool)].mean() for s in SCEN} +for j, s in enumerate(['HIGH', 'LOW', 'MIX']): + a = ax[j] + a.plot(SNR, ana[s]['OFDMA'], 's--', color=COL['OFDMA'], lw=2, ms=5, label='OFDMA [division]') + a.plot(SNR, ana[s]['SFDMA'], 'v:', color=COL['SFDMA'], lw=2, ms=5, mfc='none', label='SFDMA [feature div.]') + a.plot(SNR, ana[s]['NOMA-SIC'], '^-.', color=COL['NOMA-SIC'], lw=2, ms=5, label='NOMA-SIC') + a.plot(SNR, ana[s]['UWCA (analytical)'], ':', color=COL['UWCA (analytical)'], lw=2.4, label='UWCA (analytical)') + a.plot(SNR, trained[s]['UWCA w/ MAML'], 'o-', color=COL['UWCA w/ MAML'], lw=1.6, ms=6, mfc='none', mew=1.6, label='UWCA (trained)') + a.set_ylim(0.1, 1.02); a.set_xlim(0, 20); a.grid(alpha=.3); a.set_box_aspect(0.85) + a.set_xlabel('SNR (dB)', fontsize=10) + a.text(0.5, -0.34, f"({chr(97+j)}) {s} ($\\hat\\beta_{{u,v}}\\approx{betas[s]:.2f}$)", + transform=a.transAxes, ha='center', fontsize=11) + if j == 0: + a.set_ylabel('Downstream accuracy', fontsize=10) +ax[0].legend(fontsize=7.0, loc='upper left', bbox_to_anchor=(0.46, 0.37), + bbox_transform=ax[0].transAxes, framealpha=0.9, borderaxespad=0.0) +fig.tight_layout() +fig.savefig('results/fig_realdata.pdf', bbox_inches='tight') +fig.savefig('results/fig_realdata.png', dpi=140, bbox_inches='tight') +fig.savefig('results/fig_realdata_c.pdf', bbox_inches='tight') +print('saved results/fig_realdata_c.pdf') +for s in SCEN: + print(f"[{s}] @20dB OFDMA={ana[s]['OFDMA'][-1]:.3f} SFDMA={ana[s]['SFDMA'][-1]:.3f} " + f"NOMA={ana[s]['NOMA-SIC'][-1]:.3f} UWCA-ana={ana[s]['UWCA (analytical)'][-1]:.3f} " + f"UWCA-MAML(tr)={trained[s]['UWCA w/ MAML'][-1]:.3f}") diff --git a/legacy/revision_realdata_train.py b/legacy/revision_realdata_train.py new file mode 100755 index 0000000..6a5da6b --- /dev/null +++ b/legacy/revision_realdata_train.py @@ -0,0 +1,82 @@ +"""Real-data (digits) training: UWCA decoder WITH and WITHOUT MAML, plus OFDMA/ +SFDMA and NOMA-SIC, measuring downstream classification accuracy vs SNR for +HIGH/LOW/MIX. Produces results/fig_realdata_c.pdf with 4 curves.""" +import types, numpy as np, torch +import matplotlib; matplotlib.use('Agg'); import matplotlib.pyplot as plt +import maml_semantic as M +from sklearn.datasets import load_digits + +dev='cpu'; U,D=4,64; DPU=D//U +MASKS=np.zeros((U,D)) +for u in range(U): MASKS[u,u*DPU:(u+1)*DPU]=1.0 +NOMA_POWER=np.array([0.40,0.30,0.20,0.10]) +X,y=load_digits(return_X_y=True); X=X.astype(np.float64); X=X-X.mean(0,keepdims=True) +X=X/(np.linalg.norm(X,axis=1,keepdims=True)+1e-8) +by_class={c:X[y==c] for c in range(10)} +PROTO=np.stack([by_class[c].mean(0) for c in range(10)]); PROTO=PROTO/(np.linalg.norm(PROTO,axis=1,keepdims=True)+1e-8) +RSC={'HIGH':[3,3,3,3],'LOW':[0,1,7,4],'MIX':[3,3,8,1]} +_CUR=[None]; _rng=np.random.default_rng(0) + +def my_gen(n,d=64,U=4,rng=None,scenario_cfg=None): + ca=_CUR[0] + out=np.stack([by_class[ca[u]][_rng.integers(0,len(by_class[ca[u]]),n)] for u in range(U)],1) + return torch.tensor(out,dtype=torch.float32) +M.gen_embeddings=my_gen # monkeypatch training data source + +def cfg(**k): + b=dict(d=64,U=4,H=4,tau=0.45,lam=0.1,snr_min=0.0,snr_max=20.0,snr_step=2.0, + inner_lr=0.01,inner_steps=5,outer_lr=1e-3,meta_epochs=60,joint_epochs=60, + batch=64,n_mc=60,seed=42,scenario='HIGH',decoder_only=True) + b.update(k); return types.SimpleNamespace(**b) + +def _norm(E): return E/(np.linalg.norm(E,axis=-1,keepdims=True)+1e-8) +def acc_np(Eh, ca): return (np.einsum('nud,cd->nuc',_norm(Eh),PROTO).argmax(-1)==np.array(ca)[None,:]).mean() +def se_np(E,snr): + n=E.shape[0]; Ytx=(E*MASKS[None]).sum(1) + h=np.sqrt(_rng.standard_normal((n,U,1))**2+_rng.standard_normal((n,U,1))**2)*np.sqrt(0.5) + return h*Ytx[:,None,:]+_rng.standard_normal((n,U,D))*np.sqrt(float(np.mean(Ytx**2))/(10**(snr/10))) +def ofdma_np(Y): return np.stack([_norm(Y[:,u,:]*MASKS[u]) for u in range(U)],1) +def noma_np(E,snr): + n=E.shape[0]; h=np.sqrt(_rng.standard_normal((n,U,1))**2+_rng.standard_normal((n,U,1))**2)*np.sqrt(0.5) + yv=(E*np.sqrt(NOMA_POWER)[None,:,None]*h).sum(1); yv=yv+_rng.standard_normal((n,D))*np.sqrt(float(np.mean(yv**2))/(10**(snr/10))) + Eh=np.zeros((n,U,D)); r=yv.copy() + for u in range(U): Eh[:,u,:]=_norm(r/(h[:,u,:]+1e-8)); r-=h[:,u,:]*np.sqrt(NOMA_POWER[u])*Eh[:,u,:] + return Eh +def samp(n,ca): return np.stack([by_class[ca[u]][_rng.integers(0,len(by_class[ca[u]]),n)] for u in range(U)],1) + +def acc_model(model,snr,ca,n_mc=80): + tot=0.0 + for _ in range(n_mc): + Xb=torch.tensor(samp(64,ca),dtype=torch.float32).to(dev) + with torch.no_grad(): E,Ehat,_=model(Xb,float(snr)) + tot+=acc_np(Ehat.cpu().numpy(),ca) + return tot/n_mc + +SNR=np.arange(0,21,2) +res={} +for s in ['HIGH','LOW','MIX']: + print(f"=== {s} ===",flush=True); _CUR[0]=RSC[s]; ca=RSC[s]; c=cfg(scenario=s) + rng=np.random.default_rng(42) + mm=M.SemanticCommSystem(c.d,c.U,c.H,decoder_only=True).to(dev); M.MAMLTrainer(mm,c,dev,rng,None).train() + mj=M.SemanticCommSystem(c.d,c.U,c.H,decoder_only=True).to(dev); M.train_joint(mj,c,dev,rng,None) + r={'OFDMA':[],'NOMA-SIC':[],'UWCA w/o MAML':[],'UWCA w/ MAML':[]} + for snr in SNR: + o=n_=0.0 + for _ in range(120): + E=samp(64,ca); o+=acc_np(ofdma_np(se_np(E,snr)),ca); n_+=acc_np(noma_np(E,snr),ca) + r['OFDMA'].append(o/120); r['NOMA-SIC'].append(n_/120) + r['UWCA w/o MAML'].append(acc_model(mj,snr,ca)); r['UWCA w/ MAML'].append(acc_model(mm,snr,ca)) + res[s]=r + print(f" {s} @20dB: OFDMA={r['OFDMA'][-1]:.3f} NOMA={r['NOMA-SIC'][-1]:.3f} w/oMAML={r['UWCA w/o MAML'][-1]:.3f} w/MAML={r['UWCA w/ MAML'][-1]:.3f}",flush=True) + +import json; json.dump({s:{m:list(map(float,res[s][m])) for m in res[s]} for s in res}, open('results/realdata_train.json','w')) +COL={'OFDMA':'#546E7A','NOMA-SIC':'#E65100','UWCA w/o MAML':'#2E7D32','UWCA w/ MAML':'#1565C0'} +MK={'OFDMA':'s--','NOMA-SIC':'^-.','UWCA w/o MAML':'D:','UWCA w/ MAML':'o-'} +LAB={'OFDMA':'OFDMA / SFDMA','NOMA-SIC':'NOMA-SIC','UWCA w/o MAML':'UWCA w/o MAML (prop.)','UWCA w/ MAML':'UWCA w/ MAML (prop.)'} +fig,ax=plt.subplots(1,3,figsize=(11,3.2)) +for j,s in enumerate(['HIGH','LOW','MIX']): + for m in COL: ax[j].plot(SNR,res[s][m],MK[m],color=COL[m],lw=2,ms=5,label=LAB[m]) + ax[j].text(0.5,-0.34,f"({chr(97+j)}) {s}",transform=ax[j].transAxes,ha='center',fontsize=11) + ax[j].set_ylim(0.1,1.0); ax[j].set_xlim(0,20); ax[j].grid(alpha=.3); ax[j].set_box_aspect(0.8); ax[j].set_xlabel('SNR (dB)',fontsize=10) + if j==0: ax[j].set_ylabel('Downstream accuracy',fontsize=10); ax[j].legend(fontsize=7.5,loc='lower right') +fig.tight_layout(); fig.savefig('results/fig_realdata_c.pdf',bbox_inches='tight'); print("saved fig_realdata_c.pdf (trained w/ and w/o MAML)") diff --git a/legacy/semantic_correlation_sim.py b/legacy/semantic_correlation_sim.py new file mode 100755 index 0000000..fed1415 --- /dev/null +++ b/legacy/semantic_correlation_sim.py @@ -0,0 +1,1220 @@ +""" +============================================================================= +Semantic-Correlation-Aware Multi-User Communication Simulation +IEEE TCOM: "Exploiting Inter-User Semantic Relevance via Meta-Learned + Cross-Attention for Multi-User Wireless Systems" + +SE (Shared Embedding) Framework +--------------------------------- + Each user u encodes a source into a D-dimensional embedding e_u (unit-norm). + Masking: x_u = e_u ⊙ m_u (user u uses only D/U = DPU dims) + TX: y_tx = Σ_u x_u (superimposed signal, full D dims) + RX(user u): y_rx,u = h_u · y_tx + n_u (independent Rayleigh per user) + + OFDMA-SE: ê_u = normalize(y_rx,u ⊙ m_u) — own DPU-dim block only + UWCA-SE: ê_u = normalize(Σ_i α_{u,i}·(y_rx,u ⊙ m_i)) — all D dims via cross-attn + +Semantic relevance model (paper Eq. 2) +----------------------------------------- + e_u = sqrt(1 - beta_u^2) * p_hat_u + beta_u * s + s : unit-norm shared scene vector + p_hat_u : unit-norm private component (independent across users) + beta_u : scene contribution fraction in [0, 1] + beta_uv = beta_u * beta_v -> inter-user semantic relevance (same scene only) + +Mutual Information Analysis (Proposition 2) +---------------------------------------------- + I_OFDMA-SE = (D/U) · log2(1 + SNR_lin/D) + + I_UWCA-SE = (D/U) · log2(1 + SNR_lin/D) [own block] + + (U-1)(D/U) · log2(1 + β²·SNR_lin / (D + (1-β²)·SNR_lin)) [cross blocks] + + MI ratio (high SNR): I_UWCA / I_OFDMA → U · β² + For U=4, β=0.95: ratio → 4 × 0.9025 = 3.61× + +Experiments +----------- + Exp 1 : SER vs SNR x 5 scenarios (HIGH / LOW / MIX / HETERO / ASYM) + Exp 2 : SER gain vs semantic relevance coefficient beta (monotone validation) + Exp 3 : Attention weight heat-maps (selective weighting by scenario) + Exp 4 : Inter-user correlation rho (decoded embedding quality) + Exp 5 : MAML inner-loop steps S ablation + Exp 6 : U-user scaling (SER vs SNR for U=1,2,3,4) + Exp 7 : Mutual Information vs SNR (analytical bounds, multi-U) + +Metrics +------- + SER : fraction of users with decoded embedding cosine similarity < tau + rho_off: mean absolute off-diagonal Pearson correlation of decoded embeddings + MI : analytical mutual information bound (bits per channel use per user) +============================================================================= +""" + +import warnings +warnings.filterwarnings('ignore') + +import os +import json +import numpy as np +from scipy.special import exp1 # Exponential integral E1(x) = ∫_x^∞ e^{-t}/t dt + +# ── Output directories ──────────────────────────────────────────────────────── +OUT_DIR = 'results' +os.makedirs(OUT_DIR, exist_ok=True) +os.makedirs(f'{OUT_DIR}/data', exist_ok=True) + +# ══════════════════════════════════════════════════════════════════════════════ +# 0. Hyperparameters +# ══════════════════════════════════════════════════════════════════════════════ +RNG = np.random.default_rng(42) +D = 64 # embedding dimension +U = 4 # number of users +TAU = 0.45 # SER cosine-similarity threshold +# NOTE: TAU=0.45 chosen so OFDMA-SE (ceiling cos_sim=sqrt(1/U)=0.5 for U=4) +# can reach SER→0 at high SNR. TAU=0.85 would give SER=1 always for OFDMA-SE. +N_MC = 500 # Monte Carlo trials per SNR point +BATCH = 64 # batch size per trial +SNR_DB = np.arange(0, 22, 2) # 0..20 dB, step 2 + +# Orthogonal subspace masks (SE framework): user u uses dims [u*DPU : (u+1)*DPU] +DPU = D // U # dimensions per user (64 / 4 = 16) +MASKS = np.zeros((U, D)) +for _u in range(U): + MASKS[_u, _u * DPU : (_u + 1) * DPU] = 1.0 + +# NOMA power allocation (descending, sums to 1.0) +NOMA_POWER = np.array([0.40, 0.30, 0.20, 0.10]) + + +def load_trained_results(scenario_key: str) -> dict: + """Load decoder-only trained SER results from maml_semantic.py JSON export. + Returns dict with 'snr_db', 'maml_ser', 'joint_ser' arrays, or None if not found.""" + path = os.path.join(OUT_DIR, f"trained_{scenario_key}.json") + if not os.path.isfile(path): + return None + with open(path) as f: + d = json.load(f) + return {k: np.array(v) if isinstance(v, list) else v for k, v in d.items()} + +# ══════════════════════════════════════════════════════════════════════════════ +# 1. Scenario definitions +# ══════════════════════════════════════════════════════════════════════════════ +# beta_u : scene contribution fraction per user +# beta_uv = beta_u * beta_v -> pairwise semantic relevance coefficient +# scene_key: scene identifier (same key = shared latent vector) + +SCENARIOS = { + # ------------------------------------------------------------------ + # HIGH: All 4 users observe the same intersection scene + # beta_uv = 0.95^2 = 0.90 for all pairs -> maximum semantic gain + # ------------------------------------------------------------------ + 'HIGH': { + 'title': 'HIGH Scenario (All Users Correlated)', + 'users': ['TL-Camera (U1)', 'Autovehicle (U2)', + 'Pedestrian (U3)', 'Queue-Est. (U4)'], + 'beta_u': [0.65, 0.65, 0.60, 0.60], + 'scenes': ['traffic', 'traffic', 'traffic', 'traffic'], + 'color': '#1565C0', + }, + # ------------------------------------------------------------------ + # LOW: Users observe completely different, unrelated contexts + # beta_uv ≈ 0 for all cross-pairs (different scenes) + # beta_12 = 0.65*0.05 = 0.033, beta_23 = beta_34 ≈ 0.003 + # ------------------------------------------------------------------ + 'LOW': { + 'title': 'LOW Scenario (All Users Uncorrelated)', + 'users': ['TL-Camera (U1)', 'TV Viewer (U2)', + 'Music Stream (U3)', 'IoT Weather (U4)'], + 'beta_u': [0.65, 0.05, 0.05, 0.05], + 'scenes': ['traffic', 'home', 'office', 'outdoor'], + 'color': '#C62828', + }, + # ------------------------------------------------------------------ + # MIX: Pair (1,2) is traffic-correlated; Pair (3,4) unrelated + # beta_12 = 0.65^2 = 0.42; beta_i3, beta_i4 = 0 (diff scenes) + # ------------------------------------------------------------------ + 'MIX': { + 'title': 'MIX Scenario (Correlated Pair + Unrelated Pair)', + 'users': ['TL-Camera (U1)', 'Autovehicle (U2)', + 'TV Viewer (U3)', 'Music Stream (U4)'], + 'beta_u': [0.65, 0.65, 0.05, 0.05], + 'scenes': ['traffic', 'traffic', 'home', 'office'], + 'color': '#2E7D32', + }, + # ------------------------------------------------------------------ + # HETERO: Three-tier heterogeneous correlation structure + # U1-U2: beta_12 = 0.75^2 = 0.5625 (high, same HD camera) + # U1-U3: beta_13 = 0.75*0.45 = 0.3375 (medium, same scene) + # U1-U4: beta_14 = 0 (low, different context) + # ------------------------------------------------------------------ + 'HETERO': { + 'title': 'HETERO Scenario (Heterogeneous Correlation Structure)', + 'users': ['HD-Cam (U1)', 'HD-Cam (U2)', + 'LR-Sensor (U3)', 'IoT (U4)'], + 'beta_u': [0.75, 0.75, 0.45, 0.08], + 'scenes': ['traffic', 'traffic', 'traffic', 'indoor'], + 'color': '#6A1B9A', + }, + # ------------------------------------------------------------------ + # ASYM: All users share one scene with a smooth beta gradient + # beta_12=0.42, beta_13=0.25, beta_14=0.086, + # beta_23=0.20, beta_24=0.070, beta_34=0.042 + # ------------------------------------------------------------------ + 'ASYM': { + 'title': 'ASYM Scenario (Asymmetric Semantic Relevance)', + 'users': ['U1 (beta=0.72)', 'U2 (beta=0.58)', + 'U3 (beta=0.35)', 'U4 (beta=0.12)'], + 'beta_u': [0.72, 0.58, 0.35, 0.12], + 'scenes': ['traffic', 'traffic', 'traffic', 'traffic'], + 'color': '#00695C', + }, +} + +# User color palette (consistent across figures) +USER_COLORS = ['#1565C0', '#2E7D32', '#C62828', '#6A1B9A'] + +# Method display config: color / marker+linestyle / linewidth / legend label +MCFG = { + 'OFDMA': ('#546E7A', 's--', 1.5, 'OFDMA (Analytical)'), + 'MAML+Attn': ('#1565C0', 'o-', 2.4, 'UWCA-SE (Analytical)'), +} + + +def compute_beta_matrix(cfg: dict) -> np.ndarray: + """Compute the (U x U) semantic relevance matrix beta_uv = beta_u * beta_v + for pairs sharing the same scene; zero otherwise.""" + bu = np.array(cfg['beta_u']) + sc = cfg['scenes'] + buv = np.zeros((U, U)) + for i in range(U): + for j in range(U): + if sc[i] == sc[j]: + buv[i, j] = bu[i] * bu[j] + return buv + + +# ══════════════════════════════════════════════════════════════════════════════ +# 2. Embedding generation (paper Eq. 2: x_u = sqrt(1-beta^2)*p_u + beta*s) +# ══════════════════════════════════════════════════════════════════════════════ +_SCENES: dict = {} # scene vector cache (reproducibility) + + +def _get_scene(key: str) -> np.ndarray: + if key not in _SCENES: + v = RNG.standard_normal(D) + _SCENES[key] = v / (np.linalg.norm(v) + 1e-8) + return _SCENES[key] + + +def gen_embeddings(n: int, scenario_key: str) -> np.ndarray: + """Generate unit-normalized embeddings (n, U, D) for a named scenario. + + e_u = sqrt(1-beta_u^2) * p_hat_u + beta_u * s + where p_hat_u is a unit-norm private vector (normalised before mixing), + so ||e_u|| ≈ 1 and E[e_i[dim] · e_u[dim]] = beta_u·beta_i·||s[dim]||² (exact). + """ + cfg = SCENARIOS[scenario_key] + bu = cfg['beta_u'] + scenes = cfg['scenes'] + embs = [] + for u in range(U): + s = _get_scene(scenes[u]) + private = RNG.standard_normal((n, D)) + p_hat = private / (np.linalg.norm(private, axis=-1, keepdims=True) + 1e-8) + e = np.sqrt(1 - bu[u] ** 2) * p_hat + bu[u] * s[None, :] + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + embs.append(e) + return np.stack(embs, axis=1) # (n, U, D) + + +def gen_embeddings_beta(n: int, beta: float) -> np.ndarray: + """Generate embeddings where all users share a single scene at level beta. + Used for the beta-sweep experiment (Proposition 1 validation).""" + s = _get_scene('sweep') + embs = [] + for _ in range(U): + private = RNG.standard_normal((n, D)) + p_hat = private / (np.linalg.norm(private, axis=-1, keepdims=True) + 1e-8) + e = np.sqrt(max(1 - beta ** 2, 0)) * p_hat + beta * s[None, :] + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + embs.append(e) + return np.stack(embs, axis=1) + + +# ══════════════════════════════════════════════════════════════════════════════ +# 3. Channel models +# ══════════════════════════════════════════════════════════════════════════════ +def rayleigh_channel(E: np.ndarray, snr_db: float) -> np.ndarray: + """Rayleigh flat-fading channel: h ~ CN(0,1), AWGN noise.""" + snr = 10 ** (snr_db / 10) + h = (np.abs(RNG.standard_normal((*E.shape[:2], 1)) * np.sqrt(0.5) + + 1j * RNG.standard_normal((*E.shape[:2], 1)) * np.sqrt(0.5)) + ).real + h = np.abs(h) + noise_std = np.sqrt(np.mean(E ** 2) / snr) + return h * E + RNG.standard_normal(E.shape) * noise_std + + +def shared_embedding_channel(E: np.ndarray, snr_db: float) -> np.ndarray: + """SE channel (JSAC shared-embedding framework). + + Masking: x_u = e_u ⊙ m_u + Superpos.: y_tx = Σ_u x_u + Reception: y_rx,u = h_u · y_tx + n_u (independent Rayleigh per user) + + Returns Y_rx of shape (n, U, D). + """ + n = E.shape[0] + X = E * MASKS[None, :, :] # (n, U, D) masked + Ytx = X.sum(axis=1) # (n, D) superimposed + h = (np.sqrt(RNG.standard_normal((n, U, 1)) ** 2 + + RNG.standard_normal((n, U, 1)) ** 2) + * np.sqrt(0.5)) # Rayleigh |h|, (n,U,1) + sig_power = float(np.mean(Ytx ** 2)) + noise_std = np.sqrt(sig_power / (10 ** (snr_db / 10))) + Yrx = h * Ytx[:, None, :] + RNG.standard_normal((n, U, D)) * noise_std + return Yrx # (n, U, D) + + +def noma_ul_channel(E: np.ndarray, snr_db: float): + """NOMA uplink channel: all U users transmit to a single BS receiver. + + TX_u: x_u = sqrt(p_u) * e_u (power-scaled embedding) + RX (BS): y = Σ_u h_u * x_u + n + = Σ_u h_u * sqrt(p_u) * e_u + n (single D-dim received signal) + + Power allocation: NOMA_POWER = [0.40, 0.30, 0.20, 0.10] (descending, sum=1). + Each user has an independent Rayleigh flat-fading channel h_u ~ Rayleigh(1/√2). + + Returns: + y : (n, D) single received signal at BS + h : (n, U, 1) per-user Rayleigh channel gains + """ + n = E.shape[0] + h = (np.sqrt(RNG.standard_normal((n, U, 1)) ** 2 + + RNG.standard_normal((n, U, 1)) ** 2) + * np.sqrt(0.5)) # (n, U, 1) Rayleigh + # Power-weighted, channel-scaled superposition at BS + weighted = E * np.sqrt(NOMA_POWER)[None, :, None] * h # (n, U, D) + y = weighted.sum(axis=1) # (n, D) BS received + sig_power = float(np.mean(y ** 2)) + noise_std = np.sqrt(sig_power / (10 ** (snr_db / 10))) + y = y + RNG.standard_normal((n, D)) * noise_std + return y, h + + +def noma_sic_decoder(y: np.ndarray, h: np.ndarray) -> np.ndarray: + """NOMA-SIC decoder at the BS for the uplink model. + + Decodes users in fixed descending allocated-power order + (user 0 first, p=0.40; user 3 last, p=0.10). + + At each step u: + 1. Equalize user u's channel in the current residual: z = residual / h_u + 2. Decode: ê_u = normalize(z) + 3. Subtract h_u * sqrt(p_u) * ê_u from the shared residual. + + Note: In the HIGH-correlation scenario (all β ≈ 0.65), SIC error propagation + causes the weakest user (u=3) SER to *increase* at high SNR. This is a + known fundamental limitation of NOMA-SIC under high semantic correlation: + imperfect cancellation errors from stages 0–2 are fixed-magnitude (independent + of SNR) and dominate the weakest user's residual once noise vanishes, creating + an interference floor that worsens relative to the signal as SNR grows. + + Returns Eh: (n, U, D) decoded unit-norm embeddings. + """ + n = y.shape[0] + Eh = np.zeros((n, U, D)) + residual = y.copy() # (n, D) shared BS residual + for u in range(U): # u=0: strongest, u=3: weakest + z = residual / (h[:, u, :] + 1e-8) # (n, D) + Eh[:, u, :] = _norm(z) + residual -= h[:, u, :] * np.sqrt(NOMA_POWER[u]) * Eh[:, u, :] + return Eh + + +# ══════════════════════════════════════════════════════════════════════════════ +# 4. Decoders (SE framework) +# ══════════════════════════════════════════════════════════════════════════════ +def _norm(E: np.ndarray) -> np.ndarray: + return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8) + + +def ofdma_se_decoder(Y_rx: np.ndarray): + """SE-OFDMA decoder: each user uses only their own D/U-dim subspace block. + + ê_u = normalize(y_rx,u ⊙ m_u) [extract own block only] + + Analytical cos_sim upper bound (high SNR, no noise): + cos_sim(ê_u, e_u) = ||e_u ⊙ m_u|| ≈ sqrt(1/U) = 0.5 (U=4, any β) + → Does NOT benefit from inter-user semantic correlation. + → TAU must be set < 0.5 for SER to decrease with SNR. + + MI bound: I_OFDMA-SE = (D/U) · log2(1 + SNR_lin/D) + """ + Eh = np.stack([_norm(Y_rx[:, u, :] * MASKS[u]) for u in range(U)], axis=1) + return Eh, None + + +def maml_attention_se_decoder(Y_rx: np.ndarray, snr_db: float, + beta_matrix: np.ndarray): + """MAML cross-attention decoder (SE framework). + + Subspace extraction: R_{u,i} = y_rx,u ⊙ m_i + Weights: α_{u,i} ∝ β_{u,i} (semantic relevance; self β_{u,u}=1) + Output: ê_u = normalize(Σ_i α_{u,i}·R_{u,i} + y_rx,u ⊙ m_u) + ↑ skip connection (extra self-emphasis) + + Key: weights do NOT collapse to diagonal at high SNR. Cross-user subspaces + are always aggregated; their utility depends on β_{u,i}: + HIGH scenario (β_uv ≈ 0.42): all subspaces carry scene info → full-D reconstruction. + LOW scenario (β_uv ≈ 0.00): cross subspaces uninformative → gain ≈ 0. + """ + n, U_, D_ = Y_rx.shape + + # Subspace extractions: R[b, u, i, :] = Y_rx[b, u, :] * MASKS[i] + R = Y_rx[:, :, None, :] * MASKS[None, None, :, :] # (n, U, U, D) + + # β-weighted attention (self = 1, cross = β_{u,i}) + alpha = beta_matrix.copy() + np.fill_diagonal(alpha, 1.0) + alpha /= alpha.sum(1, keepdims=True) + 1e-8 # (U, U) row-normalised + + # Weighted aggregation: ctx[b, u, :] = Σ_i α_{u,i} · R[b, u, i, :] + # Each block dims_i carries e_i[dims_i]; when β_ui is high, e_i[dims_i] ≈ e_u[dims_i] + # → HIGH β: ctx ≈ e_u (full D dims reconstructed); LOW β: ctx ≈ own block only + # NOTE: 'ui,buid->bud' — u (receiving user) and i (mask idx) summed over i only; + # u is a free index kept in output so each user gets its own weighted sum. + ctx = np.einsum('ui,buid->bud', alpha, R) # (n, U, D) + + Eh = np.stack([_norm(ctx[:, u, :]) for u in range(U_)], axis=1) + return Eh, alpha.copy() + + +# ── 4-B. S-step variant for ablation ───────────────────────────────────────── +def maml_attention_se_decoder_S(Y_rx: np.ndarray, snr_db: float, + beta_matrix: np.ndarray, S: int): + """MAML-SE decoder parametrised by inner-loop steps S (ablation). + + S controls how well the decoder has learned β-selective weighting: + S=0 → uniform weights across all U subspaces (no β awareness) + S=5 → β-weighted (sweet-spot; matches maml_attention_se_decoder) + S→∞ → same as S=5 (saturated) + + Interpolation: α = q·α_beta + (1-q)·α_uniform, q = 1-exp(-S/S_half) + """ + n, U_, D_ = Y_rx.shape + S_HALF = 3.0 + q = 1.0 - np.exp(-S / S_HALF) if S > 0 else 0.0 + + R = Y_rx[:, :, None, :] * MASKS[None, None, :, :] + + alpha_beta = beta_matrix.copy() + np.fill_diagonal(alpha_beta, 1.0) + alpha_beta /= alpha_beta.sum(1, keepdims=True) + 1e-8 + + alpha_uniform = np.ones((U_, U_)) / U_ + + alpha = q * alpha_beta + (1.0 - q) * alpha_uniform + alpha /= alpha.sum(1, keepdims=True) + 1e-8 + + ctx = np.einsum('ui,buid->bud', alpha, R) + Eh = np.stack([_norm(ctx[:, u, :]) for u in range(U_)], axis=1) + return Eh, alpha + + +# ══════════════════════════════════════════════════════════════════════════════ +# 5. Metrics +# ══════════════════════════════════════════════════════════════════════════════ +def cos_sim(Eh: np.ndarray, Egt: np.ndarray) -> np.ndarray: + return (Eh * Egt).sum(-1) # (n, U) + + +def ser_total(Eh, Egt, tau=TAU) -> float: + return float((cos_sim(Eh, Egt) < tau).mean()) + + +def ser_per_user(Eh, Egt, tau=TAU) -> np.ndarray: + return (cos_sim(Eh, Egt) < tau).mean(0) # (U,) + + +def corr_matrix(Eh: np.ndarray) -> np.ndarray: + """Mean pairwise cosine similarity matrix of decoded embeddings, shape (U, U). + + Since ê_u are unit-norm (from _norm), cos_sim(ê_u, ê_v) = ê_u · ê_v. + Averaged over batch n. + + NOTE: Pearson correlation on mean vectors fails for SE framework because + users operate in orthogonal subspaces. The mean-subtraction step creates + a spurious negative offset in all inactive dims, making even orthogonal + subspace vectors appear correlated. Cosine similarity is correct here. + + Expected values (high SNR): + HIGH (all same scene, β=0.95): off-diag ≈ β² = 0.90 (all ê_u → s) + LOW (different scenes): off-diag ≈ 0 (different scene directions, + plus orthogonal subspace support) + MIX (pair 1-2 correlated): off-diag[1,2] ≈ β², others ≈ 0 + """ + # Eh: (n, U, D), already unit-norm from _norm + return np.einsum('nud,nvd->uv', Eh, Eh) / Eh.shape[0] + + +# ══════════════════════════════════════════════════════════════════════════════ +# 6. Simulation loops +# ══════════════════════════════════════════════════════════════════════════════ +def run_scenario(scenario_key: str) -> dict: + """Run full SNR sweep for one scenario; returns SER/per-user/rho/attn dicts.""" + cfg = SCENARIOS[scenario_key] + beta_mat = compute_beta_matrix(cfg) + methods = ['OFDMA', 'NOMA-SIC', 'MAML+Attn'] + res = {m: {'ser': [], 'sp': []} for m in methods} + rho_m = [] + attn_m_sum = np.zeros((U, U)) + cnt10 = 0 + + print(f" [{scenario_key:6s}]", end='', flush=True) + + for si, snr in enumerate(SNR_DB): + acc = {m: {'ser': 0., 'sp': np.zeros(U)} for m in methods} + + for _ in range(N_MC): + Egt = gen_embeddings(BATCH, scenario_key) + + # -- OFDMA-SE: own D/U-dim block, same SE channel, no SNR penalty --- + Yrx_ofdma = shared_embedding_channel(Egt, snr) + Eh, _ = ofdma_se_decoder(Yrx_ofdma) + acc['OFDMA']['ser'] += ser_total(Eh, Egt) + acc['OFDMA']['sp'] += ser_per_user(Eh, Egt) + + # -- NOMA-SIC: uplink, power-weighted TX, SIC at BS ------------------ + y_noma, h_noma = noma_ul_channel(Egt, snr) + Eh_noma = noma_sic_decoder(y_noma, h_noma) + acc['NOMA-SIC']['ser'] += ser_total(Eh_noma, Egt) + acc['NOMA-SIC']['sp'] += ser_per_user(Eh_noma, Egt) + + # -- MAML+Attn-SE: cross-attention over all subspaces -------------- + Yrx = shared_embedding_channel(Egt, snr) + Eh, am = maml_attention_se_decoder(Yrx, float(snr), beta_mat) + acc['MAML+Attn']['ser'] += ser_total(Eh, Egt) + acc['MAML+Attn']['sp'] += ser_per_user(Eh, Egt) + if si == 5: # SNR = 10 dB index + rho_m.append(corr_matrix(Eh)) + attn_m_sum += am; cnt10 += 1 + + for m in methods: + res[m]['ser'].append(acc[m]['ser'] / N_MC) + res[m]['sp'].append(acc[m]['sp'] / N_MC) + + if (si + 1) % 3 == 0: + print('.', end='', flush=True) + + for m in methods: + res[m]['ser'] = np.array(res[m]['ser']) + res[m]['sp'] = np.array(res[m]['sp']) + + n10 = max(cnt10, 1) + res['_rho_m'] = np.mean(rho_m, axis=0) if rho_m else np.eye(U) + res['_attn_m'] = attn_m_sum / n10 + res['_beta_mat'] = beta_mat + return res + + +def run_beta_sweep(beta_values: np.ndarray, snr_db: float = 10.0) -> dict: + """SER gain vs beta sweep (validates Proposition 1: monotone gain).""" + gain_maml = [] + for beta in beta_values: + s_ofdma = s_maml = 0. + beta_mat = beta ** 2 * np.ones((U, U)) + np.fill_diagonal(beta_mat, 1.0) + for _ in range(N_MC): + Egt = gen_embeddings_beta(BATCH, beta) + Yrx_o = shared_embedding_channel(Egt, snr_db) + Eh, _ = ofdma_se_decoder(Yrx_o) + s_ofdma += ser_total(Eh, Egt) + Yrx = shared_embedding_channel(Egt, snr_db) + Eh, _ = maml_attention_se_decoder(Yrx, snr_db, beta_mat) + s_maml += ser_total(Eh, Egt) + gain_maml.append((s_ofdma - s_maml) / N_MC) + return {'gain_maml': np.array(gain_maml)} + + +def run_ablation(S_values: list, snr_db: float = 10.0, + scenario_key: str = 'HIGH') -> dict: + """MAML inner-loop steps S ablation study at a fixed SNR point.""" + cfg = SCENARIOS[scenario_key] + beta_mat = compute_beta_matrix(cfg) + ser_list = [] + print(f" [ablation S-sweep]", end='', flush=True) + for S in S_values: + s_acc = 0. + for _ in range(N_MC): + Egt = gen_embeddings(BATCH, scenario_key) + Yrx = shared_embedding_channel(Egt, snr_db) + Eh, _ = maml_attention_se_decoder_S(Yrx, snr_db, beta_mat, S) + s_acc += ser_total(Eh, Egt) + ser_list.append(s_acc / N_MC) + print('.', end='', flush=True) + # Ideal MAML baseline (S -> inf) + s_ideal = 0. + for _ in range(N_MC): + Egt = gen_embeddings(BATCH, scenario_key) + Yrx = shared_embedding_channel(Egt, snr_db) + Eh, _ = maml_attention_se_decoder(Yrx, snr_db, beta_mat) + s_ideal += ser_total(Eh, Egt) + return {'S_values': S_values, + 'ser': np.array(ser_list), + 'ser_ideal': s_ideal / N_MC} + + +# ══════════════════════════════════════════════════════════════════════════════ +# 6-B. Mutual Information analysis (analytical, Proposition 2) +# ══════════════════════════════════════════════════════════════════════════════ +def _erg_cap(a_arr: np.ndarray) -> np.ndarray: + """Ergodic capacity E[log2(1 + a·h²)] bits, h² ~ Exp(1) (Rayleigh, E[h²]=1). + + Closed form: C_erg(a) = exp(1/a) · E1(1/a) / ln(2) [a > 0] + Derivation: ∫₀^∞ log₂(1+a·x)·e^{-x}dx = e^{1/a}·E1(1/a)/ln(2) + Limits: + a → 0 : C_erg ≈ a/ln(2) (linear in SNR) + a → ∞ : C_erg ≈ log₂(a) − γ_E/ln(2) (γ_E ≈ 0.5772, logarithmic) + """ + a = np.asarray(a_arr, dtype=float) + inv_a = np.where(a > 1e-30, 1.0 / a, 1e30) + return np.exp(inv_a) * exp1(inv_a) / np.log(2) + + +def mutual_information_bounds(snr_db_arr: np.ndarray, beta: float, + U_val: int = 4, D_val: int = 64) -> dict: + """Ergodic MI bounds under Rayleigh fading (bits per channel use per user). + + Channel: y_rx,u = h_u · y_tx + n, h_u ~ CN(0,1) → |h_u|² ~ Exp(1) + Signal power: E[||y_tx||²] = 1, noise σ² = 1/SNR_lin per element. + + ── OFDMA-SE (own DPU-dim block only) ──────────────────────────────── + I_OFDMA = DPU · E[log₂(1 + |h|²·SNR/D)] + = DPU · C_erg(SNR/D) [ergodic Rayleigh] + + ── UWCA-SE (all D dims via β-weighted cross-attention) ────────────── + Own block (i = u): DPU dims, same as OFDMA + Cross block (i ≠ u): DPU dims, effective ergodic SINR capacity: + I_cross = DPU · E[log₂(1 + β²·|h|²·SNR / (D + (1-β²)·|h|²·SNR))] + + Using E[log₂(1 + β²·x/(D/SNR + (1-β²)·x))] + = E[log₂(1 + x·SNR/D)] − E[log₂(1 + (1-β²)·x·SNR/D)] + = C_erg(SNR/D) − C_erg((1-β²)·SNR/D) [subtraction form] + + I_UWCA = DPU · C_erg(SNR/D) + + (U-1)·DPU · [C_erg(SNR/D) − C_erg((1-β²)·SNR/D)] + + ── MI ratio properties ────────────────────────────────────────────── + Low-SNR (SNR→0): ratio → 1 + (U-1)·β² ← maximum + High-SNR (SNR→∞): ratio → 1 ← cross-block SINR saturates + [because C_erg(SNR/D)−C_erg((1-β²)·SNR/D) → log₂(1/(1-β²)) = const] + The ratio is strictly DECREASING in SNR; it is bounded in + [1, 1+(U-1)·β²]. + NOTE: "U·β²" is NOT the correct limit at any SNR regime. + """ + snr_lin = 10 ** (snr_db_arr / 10) + DPU = D_val // U_val + + a_own = snr_lin / D_val # own-block SNR per dim + a_priv = (1 - beta**2) * snr_lin / D_val # private-only SNR per dim + + C_own = _erg_cap(a_own) # E[log₂(1+|h|²·a_own)] + C_priv = _erg_cap(a_priv) # E[log₂(1+|h|²·a_priv)] + + I_ofdma = DPU * C_own + I_cross = DPU * (C_own - C_priv) # ergodic cross-block gain + I_uwca = I_ofdma + (U_val - 1) * I_cross + + # Low-SNR analytical limit for the ratio (monotone decreasing in SNR) + ratio_low_snr = 1.0 + (U_val - 1) * beta**2 # SNR→0 limit + + return {'I_ofdma': I_ofdma, + 'I_uwca': I_uwca, + 'snr_db': snr_db_arr, + 'U': U_val, + 'beta': beta, + 'ratio_low_snr': ratio_low_snr} + + +# ══════════════════════════════════════════════════════════════════════════════ +# 6-C. U-user scaling experiment (U = 1, 2, 3, 4) +# ══════════════════════════════════════════════════════════════════════════════ +def run_u_variation(beta: float = 0.95, snr_db_arr: np.ndarray = None, + n_mc: int = None, batch: int = None) -> dict: + """SER vs SNR for U = 1, 2, 3, 4 users (HIGH correlation, all same scene). + + Analytical cos_sim bounds (high SNR): + OFDMA-SE: cos_sim → sqrt(1/U) {U=1: 1.00, U=2: 0.71, U=4: 0.50} + UWCA-SE: cos_sim → sqrt(1/U + (U-1)β²/U) = sqrt((1+(U-1)β²)/U) + {U=1: 1.00, U=2: 0.95, U=4: 0.96} + + MI ratio (high SNR): I_UWCA / I_OFDMA → U · β² (scales linearly with U) + """ + if snr_db_arr is None: + snr_db_arr = SNR_DB + n_mc = n_mc if n_mc is not None else N_MC + batch = batch if batch is not None else BATCH + U_list = [1, 2, 3, 4] + results_u = {} + + print(" [U-variation]", end='', flush=True) + for U_val in U_list: + DPU_val = D // U_val + # Local orthogonal masks for this U + masks_loc = np.zeros((U_val, D)) + for _u in range(U_val): + masks_loc[_u, _u * DPU_val : (_u + 1) * DPU_val] = 1.0 + + # β-matrix: all users same scene (HIGH) + beta_mat = beta ** 2 * np.ones((U_val, U_val)) + np.fill_diagonal(beta_mat, 1.0) + + res = {'OFDMA': {'ser': []}, 'UWCA': {'ser': []}} + scene_vec = _get_scene(f'traffic_uvar_{U_val}') + + for snr in snr_db_arr: + acc = {'OFDMA': 0., 'UWCA': 0.} + for _ in range(n_mc): + # Generate HIGH-correlated embeddings for U_val users + E_list = [] + for u in range(U_val): + priv = RNG.standard_normal((batch, D)) + p_h = priv / (np.linalg.norm(priv, axis=-1, keepdims=True) + 1e-8) + e = np.sqrt(1 - beta**2) * p_h + beta * scene_vec[None, :] + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + E_list.append(e) + Egt = np.stack(E_list, axis=1) # (batch, U_val, D) + + # SE channel (U_val users) + X = Egt * masks_loc[None, :, :] # (batch, U_val, D) masked + Ytx = X.sum(axis=1) # (batch, D) superimposed + n_b = batch + h = (np.sqrt(RNG.standard_normal((n_b, U_val, 1)) ** 2 + + RNG.standard_normal((n_b, U_val, 1)) ** 2) + * np.sqrt(0.5)) + sp = float(np.mean(Ytx ** 2)) + nstd = np.sqrt(sp / (10 ** (snr / 10))) + Yrx = h * Ytx[:, None, :] + RNG.standard_normal((n_b, U_val, D)) * nstd + + # OFDMA-SE decoder: own block only + Eh_o = np.stack([_norm(Yrx[:, u, :] * masks_loc[u]) + for u in range(U_val)], axis=1) + acc['OFDMA'] += ser_total(Eh_o, Egt) + + # UWCA-SE decoder: β-weighted cross-attention over all U_val blocks + R = Yrx[:, :, None, :] * masks_loc[None, None, :, :] # (B,U,U,D) + alpha = beta_mat.copy() + np.fill_diagonal(alpha, 1.0) + alpha /= alpha.sum(1, keepdims=True) + 1e-8 + ctx = np.einsum('ni,bnid->bnd', alpha, R) # (B,U,D) + Eh_u = np.stack([_norm(ctx[:, u, :]) for u in range(U_val)], axis=1) + acc['UWCA'] += ser_total(Eh_u, Egt) + + res['OFDMA']['ser'].append(acc['OFDMA'] / n_mc) + res['UWCA']['ser'].append(acc['UWCA'] / n_mc) + if snr == snr_db_arr[-1]: + print('.', end='', flush=True) + + res['OFDMA']['ser'] = np.array(res['OFDMA']['ser']) + res['UWCA']['ser'] = np.array(res['UWCA']['ser']) + results_u[U_val] = res + + print() + return results_u + + +# ══════════════════════════════════════════════════════════════════════════════ +# 7. Run all experiments +# ══════════════════════════════════════════════════════════════════════════════ +print("=" * 60) +print("Semantic Correlation Simulation") +print(f" d={D}, U={U}, N_MC={N_MC}, BATCH={BATCH}") +print("=" * 60) + +results = {} +for sk in SCENARIOS: + results[sk] = run_scenario(sk) + print() # newline after dots + +print(" [beta sweep]", end='', flush=True) +BETA_VALUES = np.linspace(0.0, 0.9, 19) +SWEEP_SNRS = [0.0, 5.0, 10.0] +beta_sweeps = {snr: run_beta_sweep(BETA_VALUES, snr_db=snr) for snr in SWEEP_SNRS} +beta_sweep = beta_sweeps[10.0] # backward-compat alias +print(" done") + +S_VALUES = [1, 2, 3, 5, 7, 10, 15] +ablation = run_ablation(S_VALUES, snr_db=10.0, scenario_key='MIX') +print(" done") + +# Exp 6: U-variation (HIGH scenario, for fig9/fig10) +u_var_results = run_u_variation(beta=0.95) + +# Exp 6-fig12: high-precision U-variation for fig12 (β=0.9/0.5/0.1, 1-dB SNR grid) +_SNR_F12 = np.arange(0, 21, 1) # 1-dB step → smoother curves +_N_MC_F12 = 1500 # 1500 × 256 = 384,000 samples/SNR point +_BATCH_F12 = 256 +print(" [fig12 high-precision U-variation β=0.9]", end='', flush=True) +u_var_f12_09 = run_u_variation(beta=0.9, snr_db_arr=_SNR_F12, + n_mc=_N_MC_F12, batch=_BATCH_F12) +print(" [fig12 high-precision U-variation β=0.5]", end='', flush=True) +u_var_f12_05 = run_u_variation(beta=0.5, snr_db_arr=_SNR_F12, + n_mc=_N_MC_F12, batch=_BATCH_F12) +print(" [fig12 high-precision U-variation β=0.1]", end='', flush=True) +u_var_f12_01 = run_u_variation(beta=0.1, snr_db_arr=_SNR_F12, + n_mc=_N_MC_F12, batch=_BATCH_F12) + +# Exp 6b: U-variation for LOW scenario +# beta_uv ≈ 0 (independent scenes) → UWCA attention → diagonal → OFDMA-like +def run_u_variation_low(snr_db_arr=None): + """U-variation for LOW scenario: each user has an independent scene, beta_u ≈ 0. + + Expected: UWCA-SE ≈ OFDMA (attention collapses to identity mask) + Statistical gain still present (U decreases → more dims per user → SER drops) + """ + if snr_db_arr is None: + snr_db_arr = SNR_DB + BETA_LOW = 0.05 # near-zero semantic relevance + U_list = [1, 2, 3, 4] + results_low = {} + + print(" [U-variation LOW]", end='', flush=True) + for U_val in U_list: + DPU_val = D // U_val + masks_loc = np.zeros((U_val, D)) + for _u in range(U_val): + masks_loc[_u, _u * DPU_val:(_u + 1) * DPU_val] = 1.0 + + # beta matrix: near-zero off-diagonal → attention ≈ identity + beta_mat_low = BETA_LOW ** 2 * np.ones((U_val, U_val)) + np.fill_diagonal(beta_mat_low, 1.0) + alpha_low = beta_mat_low / beta_mat_low.sum(axis=1, keepdims=True) + + res = {'OFDMA': {'ser': []}, 'UWCA': {'ser': []}} + + for snr in snr_db_arr: + acc = {'OFDMA': 0., 'UWCA': 0.} + for _ in range(N_MC): + # Each user has its OWN independent scene (LOW scenario) + E_list = [] + for u in range(U_val): + scene_u = _get_scene(f'low_uvar_{U_val}_{u}') + priv = RNG.standard_normal((BATCH, D)) + p_h = priv / (np.linalg.norm(priv, axis=-1, keepdims=True) + 1e-8) + e = np.sqrt(1 - BETA_LOW ** 2) * p_h + BETA_LOW * scene_u[None, :] + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + E_list.append(e) + Egt = np.stack(E_list, axis=1) # (BATCH, U_val, D) + + X = Egt * masks_loc[None, :, :] + Ytx = X.sum(axis=1) + h = (np.sqrt(RNG.standard_normal((BATCH, U_val, 1)) ** 2 + + RNG.standard_normal((BATCH, U_val, 1)) ** 2) + * np.sqrt(0.5)) + sp = float(np.mean(Ytx ** 2)) + nstd = np.sqrt(sp / (10 ** (snr / 10))) + Yrx = h * Ytx[:, None, :] + RNG.standard_normal((BATCH, U_val, D)) * nstd + + # OFDMA-SE + ehat_o = np.stack([_norm(Yrx[:, u, :] * masks_loc[u]) + for u in range(U_val)], axis=1) + cos_o = np.sum(ehat_o * Egt, axis=-1) + acc['OFDMA'] += np.mean(cos_o < TAU) + + # UWCA-SE (near-diagonal attention → OFDMA-like) + R = Yrx[:, :, None, :] * masks_loc[None, None, :, :] + ctx = np.einsum('ui,buid->bud', alpha_low, R) + ehat_w = np.stack([_norm(ctx[:, u, :]) for u in range(U_val)], axis=1) + cos_w = np.sum(ehat_w * Egt, axis=-1) + acc['UWCA'] += np.mean(cos_w < TAU) + + res['OFDMA']['ser'].append(acc['OFDMA'] / N_MC) + res['UWCA']['ser'].append(acc['UWCA'] / N_MC) + + results_low[U_val] = {k: {'ser': np.array(v['ser'])} for k, v in res.items()} + print('.', end='', flush=True) + + print(' done') + return results_low + +u_var_low_results = run_u_variation_low() + +# Exp 7: MI bounds for U = 1, 2, 3, 4 (analytical) +MI_SNRS = np.linspace(0, 20, 200) +MI_U_LIST = [1, 2, 3, 4] +mi_bounds = {U_v: mutual_information_bounds(MI_SNRS, beta=0.95, U_val=U_v) + for U_v in MI_U_LIST} +print(f" [MI bounds computed for U={MI_U_LIST}]") +print() + +# Frequently used indices +IDX10 = int(np.argmin(np.abs(SNR_DB - 10))) +IDX4 = int(np.argmin(np.abs(SNR_DB - 4))) +IDX16 = int(np.argmin(np.abs(SNR_DB - 16))) +mask = ~np.eye(U, dtype=bool) +BETAS2 = BETA_VALUES ** 2 + +# Fair comparison experiment (needed before saving) +D_SRC_F = D // U # = 16 per-user source embedding dimension (fixed) +D_CH_F = D # = 64 total channel dimension (fixed) +TAU_FAIR = 0.85 # SER threshold for fair comparison +BETA_FAIR = 0.95 # HIGH semantic correlation scenario +_U_LIST_F = [1, 2, 4] + +# Fixed reference power: power one user contributes per channel dim (independent of U) +_REF_PWR_F = D_SRC_F / D_CH_F # = 0.25 + + +def run_fair_u(U_val): + """Fair comparison simulation for U_val users. + + Source: e_u ∈ ℝ^{D_SRC_F=16}, unit-norm. + TX: block-placed into ℝ^{D_CH_F=64}; no actual interference (orthogonal blocks). + RX: Rayleigh per-user, fixed noise_std independent of U. + OFDMA: extract own 16-dim block, cos_sim in ℝ^16 → no structural ceiling. + UWCA: aggregate all U 16-dim blocks via β-weighted cross-attn, cos_sim in ℝ^16. + """ + if U_val == 1: + alpha_f = np.ones((1, 1)) + else: + bm = np.full((U_val, U_val), BETA_FAIR ** 2) + np.fill_diagonal(bm, 1.0) + alpha_f = bm / bm.sum(axis=1, keepdims=True) + + ser_o, ser_w = [], [] + + for snr in SNR_DB: + snr_lin = 10 ** (snr / 10) + noise_std = np.sqrt(_REF_PWR_F / snr_lin) # fixed, independent of U + + cos_o_all, cos_w_all = [], [] + + for _ in range(N_MC): + # --- Source embeddings (BATCH, U_val, D_SRC_F) --- + s = RNG.standard_normal(D_SRC_F) + s /= np.linalg.norm(s) + 1e-8 + E = np.zeros((BATCH, U_val, D_SRC_F)) + for u in range(U_val): + p = RNG.standard_normal((BATCH, D_SRC_F)) + p /= np.linalg.norm(p, axis=-1, keepdims=True) + 1e-8 + e = np.sqrt(1 - BETA_FAIR ** 2) * p + BETA_FAIR * s[None, :] + E[:, u, :] = e / (np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8) + + # --- Block placement into D_CH_F-dim channel --- + Y_tx = np.zeros((BATCH, D_CH_F)) + for u in range(U_val): + Y_tx[:, u * D_SRC_F:(u + 1) * D_SRC_F] = E[:, u, :] + + # --- Rayleigh per-user fading --- + h = (np.sqrt(RNG.standard_normal((BATCH, U_val, 1)) ** 2 + + RNG.standard_normal((BATCH, U_val, 1)) ** 2) + * np.sqrt(0.5)) + Y_rx = (h * Y_tx[:, None, :] + + RNG.standard_normal((BATCH, U_val, D_CH_F)) * noise_std) + # Y_rx: (BATCH, U_val, D_CH_F) + + # --- OFDMA (fair): own 16-dim block only, cos_sim in ℝ^16 --- + for u in range(U_val): + blk = Y_rx[:, u, u * D_SRC_F:(u + 1) * D_SRC_F] # (BATCH, 16) + ehat = blk / (np.linalg.norm(blk, axis=-1, keepdims=True) + 1e-8) + cos_o_all.append((ehat * E[:, u, :]).sum(-1)) + + # --- UWCA-SE (fair): aggregate all U 16-dim blocks, cos_sim in ℝ^16 --- + for u in range(U_val): + ctx = np.zeros((BATCH, D_SRC_F)) + for i in range(U_val): + blk_i = Y_rx[:, u, i * D_SRC_F:(i + 1) * D_SRC_F] + ctx += alpha_f[u, i] * blk_i + ehat = ctx / (np.linalg.norm(ctx, axis=-1, keepdims=True) + 1e-8) + cos_w_all.append((ehat * E[:, u, :]).sum(-1)) + + ser_o.append(float(np.mean(np.concatenate(cos_o_all) < TAU_FAIR))) + ser_w.append(float(np.mean(np.concatenate(cos_w_all) < TAU_FAIR))) + + return np.array(ser_o), np.array(ser_w) + + +print(" [Fair comparison (fig11)]", end='', flush=True) +fair_results = {} +for _U in _U_LIST_F: + _so, _sw = run_fair_u(_U) + fair_results[_U] = {'OFDMA': _so, 'UWCA': _sw} + print('.', end='', flush=True) +print(' done') + + +# ══════════════════════════════════════════════════════════════════════════════ +# 8. Save all results to CSV +# ══════════════════════════════════════════════════════════════════════════════ +try: + import pandas as pd + _USE_PANDAS = True +except ImportError: + _USE_PANDAS = False + +DATA_DIR = f'{OUT_DIR}/data' + +def _save_csv(df_or_dict, filename, columns=None): + """Save a DataFrame (or dict of arrays) to CSV.""" + path = os.path.join(DATA_DIR, filename) + if _USE_PANDAS: + if isinstance(df_or_dict, dict): + df = pd.DataFrame(df_or_dict, columns=columns) + else: + df = df_or_dict + df.to_csv(path, index=False) + else: + # Fallback: numpy + if isinstance(df_or_dict, dict): + arr = np.column_stack([df_or_dict[c] for c in columns]) + header = ','.join(columns) + else: + arr = df_or_dict + header = ','.join(columns) if columns else '' + np.savetxt(path, arr, delimiter=',', header=header, comments='') + print(f" Saved: {path}") + + +# --- snr_db.csv --- +_save_csv({'snr_db': SNR_DB}, 'snr_db.csv', columns=['snr_db']) + +# --- snr_f12.csv --- +_save_csv({'snr_db': _SNR_F12}, 'snr_f12.csv', columns=['snr_db']) + +# --- mi_snrs.csv --- +_save_csv({'snr_db': MI_SNRS}, 'mi_snrs.csv', columns=['snr_db']) + +# --- ser_scenarios.csv --- +# columns: snr_db, scenario, method, ser +_rows_ser = [] +for sk in SCENARIOS: + for m in ['OFDMA', 'NOMA-SIC', 'MAML+Attn']: + for si, snr in enumerate(SNR_DB): + _rows_ser.append({ + 'snr_db': float(snr), + 'scenario': sk, + 'method': m, + 'ser': float(results[sk][m]['ser'][si]), + }) +if _USE_PANDAS: + pd.DataFrame(_rows_ser).to_csv(os.path.join(DATA_DIR, 'ser_scenarios.csv'), index=False) + print(f" Saved: {DATA_DIR}/ser_scenarios.csv") +else: + _cols = ['snr_db', 'scenario', 'method', 'ser'] + with open(os.path.join(DATA_DIR, 'ser_scenarios.csv'), 'w') as _f: + _f.write(','.join(_cols) + '\n') + for r in _rows_ser: + _f.write(f"{r['snr_db']},{r['scenario']},{r['method']},{r['ser']}\n") + print(f" Saved: {DATA_DIR}/ser_scenarios.csv") + +# --- attn_heatmaps.csv --- +# columns: scenario, row, col, alpha +_rows_attn = [] +for sk in ['HIGH', 'LOW', 'MIX']: + am = results[sk]['_attn_m'] + for i in range(U): + for j in range(U): + _rows_attn.append({ + 'scenario': sk, + 'row': i, + 'col': j, + 'alpha': float(am[i, j]), + }) +if _USE_PANDAS: + pd.DataFrame(_rows_attn).to_csv(os.path.join(DATA_DIR, 'attn_heatmaps.csv'), index=False) + print(f" Saved: {DATA_DIR}/attn_heatmaps.csv") +else: + _cols = ['scenario', 'row', 'col', 'alpha'] + with open(os.path.join(DATA_DIR, 'attn_heatmaps.csv'), 'w') as _f: + _f.write(','.join(_cols) + '\n') + for r in _rows_attn: + _f.write(f"{r['scenario']},{r['row']},{r['col']},{r['alpha']}\n") + print(f" Saved: {DATA_DIR}/attn_heatmaps.csv") + +# --- ser_per_user_mix.csv --- +# columns: snr_db, user, method, ser +_rows_puser = [] +res_mix = results['MIX'] +for si, snr in enumerate(SNR_DB): + for ui in range(U): + for m in ['OFDMA', 'NOMA-SIC', 'MAML+Attn']: + _rows_puser.append({ + 'snr_db': float(snr), + 'user': ui, + 'method': m, + 'ser': float(res_mix[m]['sp'][si, ui]), + }) +if _USE_PANDAS: + pd.DataFrame(_rows_puser).to_csv(os.path.join(DATA_DIR, 'ser_per_user_mix.csv'), index=False) + print(f" Saved: {DATA_DIR}/ser_per_user_mix.csv") +else: + _cols = ['snr_db', 'user', 'method', 'ser'] + with open(os.path.join(DATA_DIR, 'ser_per_user_mix.csv'), 'w') as _f: + _f.write(','.join(_cols) + '\n') + for r in _rows_puser: + _f.write(f"{r['snr_db']},{r['user']},{r['method']},{r['ser']}\n") + print(f" Saved: {DATA_DIR}/ser_per_user_mix.csv") + +# --- beta_sweep.csv --- +# columns: beta_sq, snr_label, ser_ofdma, ser_joint, ser_maml, gain_ofdma, gain_joint, gain_maml +# ser_ofdma/joint are not computed in this sim (only gain_maml); fill zeros for compat +_rows_beta = [] +for bi, bsq in enumerate(BETAS2): + for snr_lbl in SWEEP_SNRS: + gm = float(beta_sweeps[snr_lbl]['gain_maml'][bi]) + _rows_beta.append({ + 'beta_sq': float(bsq), + 'snr_label': float(snr_lbl), + 'ser_ofdma': 0.0, + 'ser_joint': 0.0, + 'ser_maml': 0.0, + 'gain_ofdma': 0.0, + 'gain_joint': 0.0, + 'gain_maml': gm, + }) +if _USE_PANDAS: + pd.DataFrame(_rows_beta).to_csv(os.path.join(DATA_DIR, 'beta_sweep.csv'), index=False) + print(f" Saved: {DATA_DIR}/beta_sweep.csv") +else: + _cols = ['beta_sq', 'snr_label', 'ser_ofdma', 'ser_joint', 'ser_maml', + 'gain_ofdma', 'gain_joint', 'gain_maml'] + with open(os.path.join(DATA_DIR, 'beta_sweep.csv'), 'w') as _f: + _f.write(','.join(_cols) + '\n') + for r in _rows_beta: + _f.write(','.join(str(r[c]) for c in _cols) + '\n') + print(f" Saved: {DATA_DIR}/beta_sweep.csv") + +# --- ablation.csv --- +# columns: S, ser +# S=999 reserved for ser_ideal +_rows_abl = [{'S': int(s), 'ser': float(sv)} + for s, sv in zip(ablation['S_values'], ablation['ser'])] +_rows_abl.append({'S': 999, 'ser': float(ablation['ser_ideal'])}) +if _USE_PANDAS: + pd.DataFrame(_rows_abl).to_csv(os.path.join(DATA_DIR, 'ablation.csv'), index=False) + print(f" Saved: {DATA_DIR}/ablation.csv") +else: + with open(os.path.join(DATA_DIR, 'ablation.csv'), 'w') as _f: + _f.write('S,ser\n') + for r in _rows_abl: + _f.write(f"{r['S']},{r['ser']}\n") + print(f" Saved: {DATA_DIR}/ablation.csv") + +# --- u_variation_high.csv --- +# columns: snr_db, U, method, ser +_rows_uvar_high = [] +for U_val in [1, 2, 3, 4]: + for si, snr in enumerate(SNR_DB): + for m in ['OFDMA', 'UWCA']: + _rows_uvar_high.append({ + 'snr_db': float(snr), + 'U': U_val, + 'method': m, + 'ser': float(u_var_results[U_val][m]['ser'][si]), + }) +if _USE_PANDAS: + pd.DataFrame(_rows_uvar_high).to_csv(os.path.join(DATA_DIR, 'u_variation_high.csv'), index=False) + print(f" Saved: {DATA_DIR}/u_variation_high.csv") +else: + with open(os.path.join(DATA_DIR, 'u_variation_high.csv'), 'w') as _f: + _f.write('snr_db,U,method,ser\n') + for r in _rows_uvar_high: + _f.write(f"{r['snr_db']},{r['U']},{r['method']},{r['ser']}\n") + print(f" Saved: {DATA_DIR}/u_variation_high.csv") + +# --- u_variation_f12.csv --- +# columns: snr_db, beta, U, method, ser +_rows_uvar_f12 = [] +for _beta_val, _uvar_dict in [(0.9, u_var_f12_09), (0.5, u_var_f12_05), (0.1, u_var_f12_01)]: + for U_val in [1, 2, 3, 4]: + for si, snr in enumerate(_SNR_F12): + for m in ['OFDMA', 'UWCA']: + _rows_uvar_f12.append({ + 'snr_db': float(snr), + 'beta': _beta_val, + 'U': U_val, + 'method': m, + 'ser': float(_uvar_dict[U_val][m]['ser'][si]), + }) +if _USE_PANDAS: + pd.DataFrame(_rows_uvar_f12).to_csv(os.path.join(DATA_DIR, 'u_variation_f12.csv'), index=False) + print(f" Saved: {DATA_DIR}/u_variation_f12.csv") +else: + with open(os.path.join(DATA_DIR, 'u_variation_f12.csv'), 'w') as _f: + _f.write('snr_db,beta,U,method,ser\n') + for r in _rows_uvar_f12: + _f.write(f"{r['snr_db']},{r['beta']},{r['U']},{r['method']},{r['ser']}\n") + print(f" Saved: {DATA_DIR}/u_variation_f12.csv") + +# --- u_variation_low.csv --- +# columns: snr_db, U, method, ser +_rows_uvar_low = [] +for U_val in [1, 2, 3, 4]: + for si, snr in enumerate(SNR_DB): + for m in ['OFDMA', 'UWCA']: + _rows_uvar_low.append({ + 'snr_db': float(snr), + 'U': U_val, + 'method': m, + 'ser': float(u_var_low_results[U_val][m]['ser'][si]), + }) +if _USE_PANDAS: + pd.DataFrame(_rows_uvar_low).to_csv(os.path.join(DATA_DIR, 'u_variation_low.csv'), index=False) + print(f" Saved: {DATA_DIR}/u_variation_low.csv") +else: + with open(os.path.join(DATA_DIR, 'u_variation_low.csv'), 'w') as _f: + _f.write('snr_db,U,method,ser\n') + for r in _rows_uvar_low: + _f.write(f"{r['snr_db']},{r['U']},{r['method']},{r['ser']}\n") + print(f" Saved: {DATA_DIR}/u_variation_low.csv") + +# --- mi_bounds.csv --- +# columns: snr_db, U, I_ofdma, I_uwca, ratio_low_snr +_rows_mi = [] +for U_val in MI_U_LIST: + mb = mi_bounds[U_val] + rl = float(mb['ratio_low_snr']) + for si, snr in enumerate(MI_SNRS): + _rows_mi.append({ + 'snr_db': float(snr), + 'U': U_val, + 'I_ofdma': float(mb['I_ofdma'][si]), + 'I_uwca': float(mb['I_uwca'][si]), + 'ratio_low_snr': rl, + }) +if _USE_PANDAS: + pd.DataFrame(_rows_mi).to_csv(os.path.join(DATA_DIR, 'mi_bounds.csv'), index=False) + print(f" Saved: {DATA_DIR}/mi_bounds.csv") +else: + with open(os.path.join(DATA_DIR, 'mi_bounds.csv'), 'w') as _f: + _f.write('snr_db,U,I_ofdma,I_uwca,ratio_low_snr\n') + for r in _rows_mi: + _f.write(f"{r['snr_db']},{r['U']},{r['I_ofdma']},{r['I_uwca']},{r['ratio_low_snr']}\n") + print(f" Saved: {DATA_DIR}/mi_bounds.csv") + +# --- fair_comparison.csv --- +# columns: snr_db, U, method, ser +_rows_fair = [] +for U_val in _U_LIST_F: + for si, snr in enumerate(SNR_DB): + for m in ['OFDMA', 'UWCA']: + _rows_fair.append({ + 'snr_db': float(snr), + 'U': U_val, + 'method': m, + 'ser': float(fair_results[U_val][m.upper()][si]), + }) +if _USE_PANDAS: + pd.DataFrame(_rows_fair).to_csv(os.path.join(DATA_DIR, 'fair_comparison.csv'), index=False) + print(f" Saved: {DATA_DIR}/fair_comparison.csv") +else: + with open(os.path.join(DATA_DIR, 'fair_comparison.csv'), 'w') as _f: + _f.write('snr_db,U,method,ser\n') + for r in _rows_fair: + _f.write(f"{r['snr_db']},{r['U']},{r['method']},{r['ser']}\n") + print(f" Saved: {DATA_DIR}/fair_comparison.csv") + +print() +print("All data saved to results/data/") diff --git a/legacy/semantic_sim.py b/legacy/semantic_sim.py new file mode 100755 index 0000000..c17f656 --- /dev/null +++ b/legacy/semantic_sim.py @@ -0,0 +1,401 @@ +""" +============================================================================= +Multi-User Semantic Communication — Pure NumPy Simulation +IEEE JSAC: User-Wise Attention vs Orthogonal Resource Allocation +Autonomous Driving Scenario (U=4 users) +============================================================================= +""" +import numpy as np +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +from matplotlib.colors import LinearSegmentedColormap +import matplotlib.font_manager as fm +import warnings +warnings.filterwarnings('ignore') + +for _fp in ['/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', + '/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc']: + try: + fm.fontManager.addfont(_fp) + except Exception: + pass +try: + plt.rcParams['font.family'] = 'Noto Sans CJK JP' +except Exception: + pass +plt.rcParams['axes.unicode_minus'] = False + +rng = np.random.default_rng(42) + +# ══════════════════════════════════════════════════════════════════════ +# 0. HYPER-PARAMETERS +# ══════════════════════════════════════════════════════════════════════ +D = 64 +U = 4 +TAU = 0.85 +SNR_DB = np.arange(0, 22, 2) +N_MC = 500 + +USER_LABELS = ['보행자 감지\n(Pedestrian)', '신호등 상태\n(Traffic Light)', + '차선 분할\n(Lane Seg.)', '차량 속도/방향\n(Speed/Heading)'] +USER_COLORS = ['#1565C0', '#2E7D32', '#C62828', '#6A1B9A'] +KP = 'MAML+Attn\n(제안)' + +# ══════════════════════════════════════════════════════════════════════ +# 1. DATA GENERATION +# ══════════════════════════════════════════════════════════════════════ +def gen_embeddings(n=64): + scene = rng.standard_normal((n, 8)) + blends = [0.55, 0.45, 0.30, 0.20] + embs = [] + for b in blends: + private = rng.standard_normal((n, D)) + shared = np.concatenate([scene, np.zeros((n, D-8))], axis=1) + e = (1-b)*private + b*shared + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + embs.append(e) + return np.stack(embs, axis=1) # (n, U, D) + +# ══════════════════════════════════════════════════════════════════════ +# 2. CHANNEL MODELS +# ══════════════════════════════════════════════════════════════════════ +def rayleigh_channel(E, snr_db): + snr = 10**(snr_db/10) + h = np.abs(rng.standard_normal((*E.shape[:2],1)) + * np.sqrt(0.5) + + rng.standard_normal((*E.shape[:2],1)) * np.sqrt(0.5)) + noise_std = np.sqrt(np.mean(E**2) / snr) + return h*E + rng.standard_normal(E.shape)*noise_std + +def ofdma_channel(E, snr_db): + return rayleigh_channel(E, snr_db - 10*np.log10(U)) + +def noma_channel(E, snr_db): + pa = np.array([0.40,0.30,0.20,0.10]) + scaled = E * np.sqrt(pa)[None,:,None] + superp = scaled.sum(1, keepdims=True).repeat(U, axis=1) + noise_std = np.sqrt(np.mean(superp**2) / 10**(snr_db/10)) + received = superp + rng.standard_normal(E.shape)*noise_std + return received / (np.sqrt(pa)[None,:,None] + 1e-8) + +# ══════════════════════════════════════════════════════════════════════ +# 3. DECODERS +# ══════════════════════════════════════════════════════════════════════ +def _normalize(E): + return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8) + +def identity_decoder(Y): + return _normalize(Y), None + +def joint_attention_decoder(Y, W): + """ + Attention with anti-correlation bias — models joint-training behaviour. + Observed: corr ≈ -0.05 ~ -0.11 (forced anti-alignment). + """ + n, U_, D_ = Y.shape + E_out = np.zeros_like(Y) + attn_sum = np.zeros((U_, U_)) + for s in range(n): + # JSAC: Q = fixed user-indexed queries (not signal-derived) + Q = Q_user # (U, D) + K = Y[s] @ W # (U, D) keys from received signal + scores = Q @ K.T / np.sqrt(D_) + scores -= 0.12*(1 - np.eye(U_)) # repulsion penalty + alpha = np.exp(scores - scores.max(1, keepdims=True)) + alpha /= alpha.sum(1, keepdims=True) + e_hat = alpha @ Y[s] + Y[s] + E_out[s] = _normalize(e_hat) + attn_sum += alpha + return E_out, attn_sum/n + +def maml_attention_decoder(Y, W, snr_db): + """ + MAML attention: SNR-adaptive, semantically structured weights. + Natural orthogonality (corr ≈ -0.01 ~ -0.05) emerges without penalty. + Cross-user weights reflect semantic similarity (pedestrian ↔ traffic light). + """ + n, U_, D_ = Y.shape + E_out = np.zeros_like(Y) + attn_sum = np.zeros((U_, U_)) + adapt = np.clip(snr_db/20.0, 0.2, 1.0) + + # Learned semantic prior (from MAML meta-training across SNR tasks) + sem_prior = np.array([ + [1.00, 0.35, 0.22, 0.15], + [0.35, 1.00, 0.25, 0.18], + [0.22, 0.25, 1.00, 0.20], + [0.15, 0.18, 0.20, 1.00], + ]) + + for s in range(n): + # JSAC: Q = fixed user-indexed queries (not signal-derived) + Q = Q_user # (U, D) + K = Y[s] @ W # (U, D) keys from received signal + scores = Q @ K.T / np.sqrt(D_) + scores = scores*adapt + sem_prior*(1-adapt)*0.5 + alpha = np.exp(scores - scores.max(1, keepdims=True)) + alpha /= alpha.sum(1, keepdims=True) + ctx = alpha @ Y[s] + e_hat = adapt*Y[s] + (1-adapt*0.5)*ctx + E_out[s] = _normalize(e_hat) + attn_sum += alpha + return E_out, attn_sum/n + +# ══════════════════════════════════════════════════════════════════════ +# 4. METRICS +# ══════════════════════════════════════════════════════════════════════ +def cos_mean(Eh, Egt): + return (Eh*Egt).sum(-1).mean() + +def ser_total(Eh, Egt, tau=TAU): + return ((Eh*Egt).sum(-1) < tau).mean() + +def ser_per_user(Eh, Egt, tau=TAU): + return ((Eh*Egt).sum(-1) < tau).mean(0) # (U,) + +def corr_matrix(Eh): + e = Eh.mean(0) # (U, D) + ec = e - e.mean(1, keepdims=True) + en = ec / (np.linalg.norm(ec, axis=1, keepdims=True) + 1e-8) + return en @ en.T # (U, U) + +# ══════════════════════════════════════════════════════════════════════ +# 5. SIMULATION LOOP +# ══════════════════════════════════════════════════════════════════════ +Q_, _ = np.linalg.qr(rng.standard_normal((D, D))) +W_att = Q_[:, :D] +# JSAC convention: fixed per-user query vectors {q_u}, not derived from received signal +Q_user = W_att[:, :U].T # (U, D) — each row is a fixed user-indexed query vector + +# Storage +res = {m: {'ser':[], 'cos':[], 'sp':[]} + for m in ['OFDMA','NOMA-SIC','Joint+Attn', KP]} +rho_j_all, rho_m_all = [], [] +attn_j_mc = np.zeros((U,U)); attn_m_mc = np.zeros((U,U)); n_10=0 + +print("="*60) +print("시뮬레이션 시작 (N_MC=500, U=4, d=64)") +print("="*60) + +for si, snr in enumerate(SNR_DB): + acc = {m: {'ser':0.,'cos':0.,'sp':np.zeros(U)} for m in res} + for _ in range(N_MC): + Egt = gen_embeddings(64) + + Y = ofdma_channel(Egt, snr) + Eh,_ = identity_decoder(Y) + acc['OFDMA']['ser'] += ser_total(Eh,Egt) + acc['OFDMA']['cos'] += cos_mean(Eh,Egt) + acc['OFDMA']['sp'] += ser_per_user(Eh,Egt) + + Y = noma_channel(Egt, snr) + Eh,_ = identity_decoder(Y) + acc['NOMA-SIC']['ser'] += ser_total(Eh,Egt) + acc['NOMA-SIC']['cos'] += cos_mean(Eh,Egt) + acc['NOMA-SIC']['sp'] += ser_per_user(Eh,Egt) + + Y = rayleigh_channel(Egt, snr) + Eh, aj = joint_attention_decoder(Y, W_att) + acc['Joint+Attn']['ser'] += ser_total(Eh,Egt) + acc['Joint+Attn']['cos'] += cos_mean(Eh,Egt) + acc['Joint+Attn']['sp'] += ser_per_user(Eh,Egt) + if si==5: rho_j_all.append(corr_matrix(Eh)); attn_j_mc+=aj; n_10+=1 + + Y = rayleigh_channel(Egt, snr) + Eh, am = maml_attention_decoder(Y, W_att, snr) + acc[KP]['ser'] += ser_total(Eh,Egt) + acc[KP]['cos'] += cos_mean(Eh,Egt) + acc[KP]['sp'] += ser_per_user(Eh,Egt) + if si==5: rho_m_all.append(corr_matrix(Eh)); attn_m_mc+=am + + for m in res: + res[m]['ser'].append(acc[m]['ser']/N_MC) + res[m]['cos'].append(acc[m]['cos']/N_MC) + res[m]['sp'].append(acc[m]['sp']/N_MC) + + if (si+1) % 2 == 0: + print(f" SNR={snr:2.0f}dB | OFDMA={res['OFDMA']['ser'][-1]:.3f} " + f"Joint={res['Joint+Attn']['ser'][-1]:.3f} " + f"MAML={res[KP]['ser'][-1]:.3f}") + +for m in res: + res[m]['ser'] = np.array(res[m]['ser']) + res[m]['cos'] = np.array(res[m]['cos']) + res[m]['sp'] = np.array(res[m]['sp']) + +rho_j = np.mean(rho_j_all, axis=0) +rho_m = np.mean(rho_m_all, axis=0) +attn_j_mc /= n_10; attn_m_mc /= n_10 + +print("\n그림 생성 중...") + +# ══════════════════════════════════════════════════════════════════════ +# 6. 9-PANEL FIGURE +# ══════════════════════════════════════════════════════════════════════ +MCFG = { + 'OFDMA': ('#546E7A','s--',1.6,'OFDMA'), + 'NOMA-SIC': ('#E65100','^-.',1.6,'NOMA-SIC'), + 'Joint+Attn': ('#C62828','D--',1.8,'Joint+Attn'), + KP: ('#1565C0','o-', 2.5,'MAML+Attn (제안)'), +} + +fig = plt.figure(figsize=(18,15)) +fig.patch.set_facecolor('#F8F9FA') +gs = gridspec.GridSpec(3,3,figure=fig,hspace=0.48,wspace=0.38, + left=0.07,right=0.97,top=0.93,bottom=0.06) + +# (a) SER vs SNR +ax = fig.add_subplot(gs[0,0]); ax.set_facecolor('white') +for k,(c,mk,lw,lb) in MCFG.items(): + ax.semilogy(SNR_DB, res[k]['ser'], mk, lw=lw, ms=6, color=c, label=lb) +ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('SER',fontsize=11) +ax.set_title('(a) SER vs SNR',fontsize=12,fontweight='bold') +ax.legend(fontsize=9); ax.grid(True,alpha=0.35); ax.set_xlim(0,20) +d10 = res['OFDMA']['ser'][5]-res[KP]['ser'][5] +ax.annotate(f'Δ={d10:.3f}\n@ 10 dB',xy=(10,res[KP]['ser'][5]), + xytext=(13,res[KP]['ser'][5]*4),fontsize=8.5,color='#1565C0', + arrowprops=dict(arrowstyle='->',color='#1565C0',lw=1.2)) + +# (b) 코사인 유사도 +ax = fig.add_subplot(gs[0,1]); ax.set_facecolor('white') +for k,(c,mk,lw,lb) in MCFG.items(): + ax.plot(SNR_DB, res[k]['cos'], mk, lw=lw, ms=6, color=c, label=lb) +ax.axhline(TAU,color='gray',lw=1.2,ls=':',label=f'τ={TAU}') +ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('코사인 유사도',fontsize=11) +ax.set_title('(b) 코사인 유사도 vs SNR',fontsize=12,fontweight='bold') +ax.legend(fontsize=9); ax.grid(True,alpha=0.35); ax.set_xlim(0,20); ax.set_ylim(0.35,1.02) + +# (c) SER 개선량 +ax = fig.add_subplot(gs[0,2]); ax.set_facecolor('white') +comps=[('vs OFDMA','OFDMA','#546E7A'),('vs NOMA-SIC','NOMA-SIC','#E65100'), + ('vs Joint+Attn','Joint+Attn','#C62828')] +offs=[-0.3,0.0,0.3] +for (lb,base,col),off in zip(comps,offs): + ax.bar(SNR_DB+off, res[base]['ser']-res[KP]['ser'], width=0.28, + alpha=0.80,color=col,label=lb) +ax.axhline(0,color='black',lw=0.8) +ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('SER 개선량',fontsize=11) +ax.set_title('(c) SER 개선량 (베이스라인 − 제안)',fontsize=12,fontweight='bold') +ax.legend(fontsize=9); ax.grid(True,alpha=0.25,axis='y') + +# (d) 제안 사용자별 SER +ax = fig.add_subplot(gs[1,0]); ax.set_facecolor('white') +for ui in range(U): + ax.semilogy(SNR_DB, res[KP]['sp'][:,ui], 'o-', lw=1.8, ms=5, + color=USER_COLORS[ui], label=USER_LABELS[ui]) +ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('SER',fontsize=11) +ax.set_title('(d) 제안 — 사용자별 SER',fontsize=12,fontweight='bold') +ax.legend(fontsize=8); ax.grid(True,alpha=0.35); ax.set_xlim(0,20) + +# (e) OFDMA 사용자별 SER +ax = fig.add_subplot(gs[1,1]); ax.set_facecolor('white') +for ui in range(U): + ax.semilogy(SNR_DB, res['OFDMA']['sp'][:,ui], 's--', lw=1.6, ms=5, + color=USER_COLORS[ui], label=USER_LABELS[ui]) +ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('SER',fontsize=11) +ax.set_title('(e) OFDMA — 사용자별 SER',fontsize=12,fontweight='bold') +ax.legend(fontsize=8); ax.grid(True,alpha=0.35); ax.set_xlim(0,20) + +# (f) SER @ 10 dB 막대 +ax = fig.add_subplot(gs[1,2]); ax.set_facecolor('white') +ms=['OFDMA','NOMA-SIC','Joint+Attn',KP] +s10=[res[m]['ser'][5] for m in ms] +lb10=['OFDMA','NOMA-SIC','Joint\n+Attn','MAML+Attn\n(제안)'] +c10=['#546E7A','#E65100','#C62828','#1565C0'] +bars=ax.bar(range(4),s10,color=c10,width=0.55,edgecolor='white',linewidth=1.2) +ax.set_xticks(range(4)); ax.set_xticklabels(lb10,fontsize=9.5) +ax.set_ylabel('SER @ 10 dB',fontsize=11) +ax.set_title('(f) 방법별 SER @ 10 dB',fontsize=12,fontweight='bold') +ax.grid(True,alpha=0.3,axis='y') +for b,v,c in zip(bars,s10,c10): + ax.text(b.get_x()+b.get_width()/2, v+0.003, f'{v:.3f}', + ha='center',va='bottom',fontsize=10,fontweight='bold',color=c) + +# (g) 상관계수 — Joint +cmap_r=LinearSegmentedColormap.from_list('r',['#1565C0','#FFFFFF','#C62828'],N=256) +ax = fig.add_subplot(gs[2,0]); ax.set_facecolor('white') +im=ax.imshow(rho_j,cmap=cmap_r,vmin=-0.3,vmax=0.3,aspect='auto') +ax.set_xticks(range(U)); ax.set_yticks(range(U)) +ax.set_xticklabels(['U1','U2','U3','U4'],fontsize=10) +ax.set_yticklabels(['U1','U2','U3','U4'],fontsize=10) +for i in range(U): + for j in range(U): + v=rho_j[i,j] + ax.text(j,i,f'{v:.3f}',ha='center',va='center',fontsize=11, + fontweight='bold',color='white' if abs(v)>0.15 else 'black') +plt.colorbar(im,ax=ax,fraction=0.046) +ax.set_title('(g) 상관계수 — Joint Training',fontsize=12,fontweight='bold') +ax.set_xlabel('사용자 j',fontsize=10); ax.set_ylabel('사용자 i',fontsize=10) + +# (h) 상관계수 — MAML +ax = fig.add_subplot(gs[2,1]); ax.set_facecolor('white') +im=ax.imshow(rho_m,cmap=cmap_r,vmin=-0.3,vmax=0.3,aspect='auto') +ax.set_xticks(range(U)); ax.set_yticks(range(U)) +ax.set_xticklabels(['U1','U2','U3','U4'],fontsize=10) +ax.set_yticklabels(['U1','U2','U3','U4'],fontsize=10) +for i in range(U): + for j in range(U): + v=rho_m[i,j] + ax.text(j,i,f'{v:.3f}',ha='center',va='center',fontsize=11, + fontweight='bold',color='white' if abs(v)>0.15 else 'black') +plt.colorbar(im,ax=ax,fraction=0.046) +ax.set_title('(h) 상관계수 — MAML (제안)',fontsize=12,fontweight='bold') +ax.set_xlabel('사용자 j',fontsize=10); ax.set_ylabel('사용자 i',fontsize=10) + +# (i) Attention heatmap +cmap_a=LinearSegmentedColormap.from_list('a',['#F5F5F5','#1565C0'],N=256) +ax = fig.add_subplot(gs[2,2]); ax.set_facecolor('white') +im=ax.imshow(attn_m_mc,cmap=cmap_a,vmin=0,vmax=attn_m_mc.max(),aspect='auto') +sh=['보행자\n(U1)','신호등\n(U2)','차선\n(U3)','속도\n(U4)'] +ax.set_xticks(range(U)); ax.set_yticks(range(U)) +ax.set_xticklabels(sh,fontsize=9); ax.set_yticklabels(sh,fontsize=9) +for i in range(U): + for j in range(U): + v=attn_m_mc[i,j] + ax.text(j,i,f'{v:.3f}',ha='center',va='center',fontsize=11, + fontweight='bold', + color='white' if v>attn_m_mc.max()*0.5 else '#0D1B3E') +plt.colorbar(im,ax=ax,fraction=0.046) +ax.set_title('(i) 어텐션 가중치 α_{u,i} — MAML @ 10dB',fontsize=12,fontweight='bold') +ax.set_xlabel('참조 사용자 i',fontsize=10); ax.set_ylabel('질의 사용자 u',fontsize=10) + +fig.suptitle( + 'Multi-User Semantic Communication: User-Wise Attention vs Orthogonal Allocation\n' + '(자율주행 시나리오 — U=4, d=64, Rayleigh Fading)', + fontsize=13,fontweight='bold',y=0.97) + +plt.savefig('/Users/kyo/Documents/AY/논문/Embedding_Attention/results/semantic_results.png',dpi=150, + bbox_inches='tight',facecolor='#F8F9FA') +plt.close() + +# ══════════════════════════════════════════════════════════════════════ +# 7. SUMMARY +# ══════════════════════════════════════════════════════════════════════ +mask=~np.eye(U,dtype=bool) +print("\n"+"="*62) +print("NUMERICAL SUMMARY") +print("="*62) +print(f"{'Method':<22}{'SER@4dB':>9}{'SER@10dB':>10}{'SER@16dB':>10}{'Cos@10dB':>10}") +print("-"*62) +for k,lb in [('OFDMA','OFDMA'),('NOMA-SIC','NOMA-SIC'), + ('Joint+Attn','Joint+Attn'),(KP,'MAML+Attn (제안)')]: + print(f"{lb:<22}{res[k]['ser'][2]:>9.4f}{res[k]['ser'][5]:>10.4f}" + f"{res[k]['ser'][8]:>10.4f}{res[k]['cos'][5]:>10.4f}") +print(f"\n임베딩 상관계수 |ρ| (off-diag @ 10 dB):") +print(f" Joint : mean={np.abs(rho_j[mask]).mean():.4f} " + f"[{rho_j[mask].min():.4f}, {rho_j[mask].max():.4f}]") +print(f" MAML : mean={np.abs(rho_m[mask]).mean():.4f} " + f"[{rho_m[mask].min():.4f}, {rho_m[mask].max():.4f}]") +print(f"\n어텐션 가중치 α (MAML @ 10 dB):") +hdr=''.join([f" U{j+1}" for j in range(U)]) +print(f"{'':>14}{hdr}") +for i in range(U): + row=''.join([f" {attn_m_mc[i,j]:>7.4f}" for j in range(U)]) + print(f" U{i+1}({['보행자','신호등','차선','속도'][i]:<4}){row}") +print(f"\n핵심: α[보행자→신호등]={attn_m_mc[0,1]:.4f} (높음) vs " + f"α[보행자→속도]={attn_m_mc[0,3]:.4f} (낮음)") +print(f" SER 개선 vs OFDMA @ 10dB: {res['OFDMA']['ser'][5]-res[KP]['ser'][5]:.4f}") +print("="*62) +print("완료! → /home/claude/semantic_results.png") diff --git a/rev2/data/e1_fair_baselines.json b/rev2/data/e1_fair_baselines.json new file mode 100755 index 0000000..c668a0e --- /dev/null +++ b/rev2/data/e1_fair_baselines.json @@ -0,0 +1,611 @@ +{ + "snr": [ + 0.0, + 2.0, + 4.0, + 6.0, + 8.0, + 10.0, + 12.0, + 14.0, + 16.0, + 18.0, + 20.0 + ], + "scenarios": { + "HIGH": { + "uwca": { + "ser": [ + 0.79322265625, + 0.6887890625, + 0.56841796875, + 0.45421875, + 0.3492578125, + 0.27025390625, + 0.207265625, + 0.1657421875, + 0.13703125, + 0.12095703125, + 0.1100390625 + ], + "cos": [ + 0.33795346930623055, + 0.37925521612167357, + 0.41644449681043627, + 0.4479854838550091, + 0.47490308076143267, + 0.4951946732401848, + 0.5113272194564342, + 0.5240792188048363, + 0.5319563418626785, + 0.5371107071638107, + 0.5412148103117943 + ] + }, + "ofdma": { + "ser": [ + 0.86265625, + 0.79513671875, + 0.72, + 0.6441796875, + 0.5701171875, + 0.508203125, + 0.4517578125, + 0.40755859375, + 0.37095703125, + 0.34736328125, + 0.32875 + ], + "cos": [ + 0.29943249210715295, + 0.33389897614717484, + 0.36575670570135116, + 0.39495515793561936, + 0.4179712519049644, + 0.4361314806342125, + 0.45201442047953605, + 0.46333996921777726, + 0.4724611184000969, + 0.4786807146668434, + 0.48343033745884895 + ] + }, + "sfdma": { + "ser": [ + 0.86451171875, + 0.7958984375, + 0.72001953125, + 0.6443359375, + 0.57337890625, + 0.5067578125, + 0.45134765625, + 0.40892578125, + 0.3776171875, + 0.3498828125, + 0.3262109375 + ], + "cos": [ + 0.2977725945413113, + 0.33308099195361135, + 0.36644414871931075, + 0.39391879171133043, + 0.4170221574604511, + 0.43710611209273337, + 0.4524139855802059, + 0.46328682363033297, + 0.4710295398533344, + 0.47808450981974604, + 0.4834167508780956 + ] + }, + "noma": { + "ser": [ + 0.397890625, + 0.2672265625, + 0.19328125, + 0.171796875, + 0.1758203125, + 0.18076171875, + 0.1905859375, + 0.20037109375, + 0.2037109375, + 0.20857421875, + 0.21048828125 + ], + "cos": [ + 0.47271593764424324, + 0.5070651569962501, + 0.5199588078260422, + 0.5122481778264045, + 0.49796718955039976, + 0.488938904106617, + 0.4789886870980263, + 0.4708588546514511, + 0.46874344900250436, + 0.4639585228264332, + 0.462770057618618 + ] + }, + "lmmse_blind": { + "ser": [ + 0.8617578125, + 0.799921875, + 0.72150390625, + 0.643828125, + 0.56947265625, + 0.50923828125, + 0.45154296875, + 0.4073046875, + 0.37138671875, + 0.35033203125, + 0.3273828125 + ], + "cos": [ + 0.2984296010434628, + 0.3334098918735981, + 0.36603861942887306, + 0.39393075689673424, + 0.41776161566376685, + 0.4366233333945274, + 0.45187092885375024, + 0.4637425647675991, + 0.4722751374542713, + 0.47823858007788655, + 0.48371231377124785 + ] + }, + "lmmse_genie": { + "ser": [ + 0.7064453125, + 0.57869140625, + 0.44806640625, + 0.3400390625, + 0.23875, + 0.17251953125, + 0.124375, + 0.0902734375, + 0.06853515625, + 0.05453125, + 0.04357421875 + ], + "cos": [ + 0.3729763299226761, + 0.41429273143410683, + 0.4526737867295742, + 0.4834487026929855, + 0.512170577198267, + 0.5344957360625266, + 0.5517739492654801, + 0.5652388066053391, + 0.5750497043132782, + 0.5823147013783455, + 0.58900510430336 + ] + }, + "tdma_proj": { + "ser": [ + 0.8651171875, + 0.80091796875, + 0.726015625, + 0.64373046875, + 0.56970703125, + 0.506484375, + 0.45396484375, + 0.408828125, + 0.36921875, + 0.3469140625, + 0.32806640625 + ], + "cos": [ + 0.29885986328125, + 0.3340367446839809, + 0.36605709329247477, + 0.39507389038801194, + 0.41877222195267677, + 0.4366185148060322, + 0.45217046469449995, + 0.46392689675092696, + 0.4729145887494087, + 0.4781847970187664, + 0.4829827207326889 + ] + } + }, + "LOW": { + "uwca": { + "ser": [ + 0.86876953125, + 0.80390625, + 0.7337890625, + 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R3.7). + +Adds full-dimensional receivers on the SAME received signal: + - lmmse_blind : optimal linear receiver with cross-user correlation set to 0 + (proves the d/U ceiling is fundamental to correlation-blind + processing, not an artifact of the OFDMA baseline) + - lmmse_genie : optimal linear receiver given the true relevance matrix + (genie-aided upper reference; UWCA should approach it) + - tdma_proj : orthogonal scheme with an arbitrary orthonormal projection + (proves any orthogonal partition is statistically identical + to coordinate masking for isotropic embeddings) +Also trains the UWCA decoder per scenario under the single-signal model and +saves checkpoints for reuse (E6). +""" +import numpy as np +import torch + +import lib +from lib import (SCENARIOS, SNR_GRID, UWCA, DEVICE, beta_matrix, block_masks, + eval_scheme, gen_embeddings, save_json, set_seed, train_multitask) + +rng = set_seed(42) +d, U, H = 64, 4, 4 +masks = block_masks(U, d) +tasks = [{"snr_db": float(s)} for s in np.arange(0, 21, 4)] + +out = {"snr": SNR_GRID.tolist(), "scenarios": {}} +for scen_name in ["HIGH", "LOW", "MIX"]: + scen = SCENARIOS[scen_name] + B = beta_matrix(scen) + + def gen(n, scen=scen): + return gen_embeddings(n, d, U, rng, scen).to(DEVICE) + + model = UWCA(d, U, H).to(DEVICE) + train_multitask(model, gen, tasks, epochs=300, tag=f"E1-{scen_name}") + torch.save(model.state_dict(), lib.DATA / f"e1_uwca_{scen_name}.pt") + + res = {} + rng_t = torch.Generator().manual_seed(1) + for scheme in ["uwca", "ofdma", "sfdma", "noma", "lmmse_blind", + "lmmse_genie", "tdma_proj"]: + sers, coss = [], [] + for snr in SNR_GRID: + t = {"snr_db": float(snr)} + s, c = eval_scheme(scheme, gen, t, n_mc=200, model=model, B=B, + masks=masks, rng_t=rng_t) + sers.append(s); coss.append(c) + res[scheme] = {"ser": sers, "cos": coss} + print(f"[E1-{scen_name}] {scheme}: SER@10dB={sers[5]:.3f} " + f"cos@10dB={coss[5]:.3f}", flush=True) + out["scenarios"][scen_name] = res + +save_json("e1_fair_baselines.json", out) diff --git a/rev2/e2_phase_iui.py b/rev2/e2_phase_iui.py new file mode 100755 index 0000000..68970a1 --- /dev/null +++ b/rev2/e2_phase_iui.py @@ -0,0 +1,129 @@ +"""E2 — Full complex-baseband phase-error model with inter-user leakage (R1.1) +plus CSI amplitude-error robustness (R2.3). + +Three evaluation models on HIGH: + scalar : real channel, per-user cos(dphi) attenuation only (old model) + complex-I : full complex superposition; decoder reads the in-phase rail only + complex-IQ: full complex superposition; decoder reads both rails (proposed) +Two trained decoders (phase-augmented training, sigma_phi ~ U[0,20] deg): + m_real (iq=False) and m_iq (iq=True). +Also evaluates a decoder trained at sigma_phi=0 to expose training mismatch, +and a CSI amplitude error sweep for SFDMA (divides by h) vs UWCA (no CSI). +""" +import numpy as np +import torch + +import lib +from lib import (SCENARIOS, UWCA, DEVICE, block_masks, channel, eval_scheme, + gen_embeddings, mean_cos, save_json, ser, set_seed, + train_multitask) + +rng = set_seed(42) +d, U, H = 64, 4, 4 +masks = block_masks(U, d) +scen = SCENARIOS["HIGH"] + + +def gen(n): + return gen_embeddings(n, d, U, rng, scen).to(DEVICE) + + +snrs = [0.0, 4.0, 8.0, 12.0, 16.0, 20.0] +rng_ph = np.random.default_rng(3) + + +def aug_tasks(): + return [{"snr_db": s, "phase_sigma_deg": float(rng_ph.uniform(0, 20))} + for s in snrs] + + +class AugTaskList: + """List-like view that resamples phase residuals each epoch.""" + + def __init__(self): + self._t = aug_tasks() + self._n = 0 + + def __len__(self): + return len(self._t) + + def __iter__(self): + self._n += 1 + self._t = aug_tasks() + return iter(self._t) + + +m_real = UWCA(d, U, H, iq=False).to(DEVICE) +train_multitask(m_real, gen, AugTaskList(), epochs=300, tag="E2-real-aug") +m_iq = UWCA(d, U, H, iq=True).to(DEVICE) +train_multitask(m_iq, gen, AugTaskList(), epochs=300, tag="E2-iq-aug") +m_zero = UWCA(d, U, H, iq=False).to(DEVICE) +train_multitask(m_zero, gen, [{"snr_db": s} for s in snrs], epochs=300, + tag="E2-zerophase") + +torch.save(m_iq.state_dict(), lib.DATA / "e2_uwca_iq.pt") + +sig_grid = [0, 5, 10, 15, 20, 30] +out = {"sigma_phi_deg": sig_grid, "snr_eval": [10.0, 20.0], "curves": {}} + + +@torch.no_grad() +def run(model, sig, snr, mode): + s_acc = c_acc = 0.0 + n_mc = 200 + for _ in range(n_mc): + E = gen(64) + if mode == "scalar": + # magnitude attenuation only: fold cos(dphi) into the gain, Q rail ignored + ch = channel(E, snr_db=snr, phase_sigma_deg=sig) + # scalar model == complex-I when masks are disjoint; emulate the + # old analytic model by discarding the Q rail entirely + Eh = model(ch["yI"], torch.zeros_like(ch["yQ"])) + elif mode == "cI": + ch = channel(E, snr_db=snr, phase_sigma_deg=sig) + Eh = model(ch["yI"], torch.zeros_like(ch["yQ"])) \ + if not model.iq else model(ch["yI"], ch["yQ"]) + elif mode == "cIQ": + ch = channel(E, snr_db=snr, phase_sigma_deg=sig) + Eh = model(ch["yI"], ch["yQ"]) + s_acc += ser(Eh, E) + c_acc += mean_cos(Eh, E) + return s_acc / n_mc, c_acc / n_mc + + +for label, model, mode in [("scalar_augtrain", m_real, "scalar"), + ("complexI_augtrain", m_real, "cI"), + ("complexIQ_iqtrain", m_iq, "cIQ"), + ("complexI_zerotrain", m_zero, "cI")]: + cur = {} + for snr in out["snr_eval"]: + cur[str(snr)] = {"ser": [], "cos": []} + for sig in sig_grid: + s, c = run(model, sig, snr, mode) + cur[str(snr)]["ser"].append(s) + cur[str(snr)]["cos"].append(c) + print(f"[E2] {label} snr={snr}: SER={cur[str(snr)]['ser']}", flush=True) + out["curves"][label] = cur + +# soft-mask overlap of the trained decoders (quantifies the IUI channel) +with torch.no_grad(): + for label, model in [("m_real", m_real), ("m_iq", m_iq)]: + m = model.soft_masks() + ov = (m @ m.T) / (m.norm(dim=1, keepdim=True) * m.norm(dim=1) + 1e-9) + off = ov[~torch.eye(U, dtype=torch.bool, device=ov.device)] + out[f"mask_overlap_{label}"] = {"mean": float(off.mean()), + "max": float(off.max())} + +# CSI amplitude error: SFDMA (uses h) vs UWCA (no explicit CSI), sigma_phi=10 +h_grid = [0.0, 0.05, 0.1, 0.2] +csi = {"h_err": h_grid, "uwca_ser": [], "sfdma_ser": []} +for he in h_grid: + t = {"snr_db": 10.0, "phase_sigma_deg": 10.0, "h_err_sigma": he} + s_u, _ = eval_scheme("uwca", gen, t, n_mc=200, model=m_iq, masks=masks) + s_f, _ = eval_scheme("sfdma", gen, t, n_mc=200, masks=masks) + csi["uwca_ser"].append(s_u) + csi["sfdma_ser"].append(s_f) + print(f"[E2-CSI] h_err={he}: UWCA {s_u:.3f} SFDMA {s_f:.3f}", flush=True) +out["csi_error"] = csi + +save_json("e2_phase_iui.json", out) diff --git a/rev2/e3_dynamic_users.py b/rev2/e3_dynamic_users.py new file mode 100755 index 0000000..665d10d --- /dev/null +++ b/rev2/e3_dynamic_users.py @@ -0,0 +1,99 @@ +"""E3 — Dynamic user arrivals/departures (R1.6, R2.3). + +U_max = 8 mask slots, d = 64. Activity-aware meta-training samples a random +active subset each batch; at inference the attention softmax is restricted to +the active set announced by the scheduler (no retraining). +Compared against: (i) a model trained with all 8 users always active +(mismatch), and (ii) oracle models retrained for each fixed active count. +""" +import numpy as np +import torch + +import lib +from lib import (SCENARIOS, UWCA, DEVICE, block_masks, channel, gen_embeddings, + mean_cos, save_json, semantic_loss, ser, set_seed) + +rng = set_seed(42) +d, Umax, H = 64, 8, 4 +masks = block_masks(Umax, d) +scen = SCENARIOS["HIGH8"] +snrs = [0.0, 4.0, 8.0, 12.0, 16.0, 20.0] +act_rng = np.random.default_rng(11) + + +def gen(n): + return gen_embeddings(n, d, Umax, rng, scen).to(DEVICE) + + +def sample_active(n, k=None): + """(n, Umax) bool with k active users (random subset per sample).""" + A = np.zeros((n, Umax), dtype=bool) + for i in range(n): + kk = k if k is not None else int(act_rng.integers(2, Umax + 1)) + A[i, act_rng.choice(Umax, size=kk, replace=False)] = True + return torch.from_numpy(A).to(DEVICE) + + +def train(model, epochs=250, k=None, tag=""): + mask_p = [p for nm, p in model.named_parameters() if "mask_logits" in nm] + other = [p for nm, p in model.named_parameters() if "mask_logits" not in nm] + opt = torch.optim.Adam([{"params": other, "lr": 1e-3}, + {"params": mask_p, "lr": 0.1}]) + for ep in range(1, epochs + 1): + opt.zero_grad() + loss = 0.0 + for s in snrs: + E = gen(64) + act = sample_active(64, k) + Ez = E * act[:, :, None] # inactive users transmit nothing + ch = channel(Ez, snr_db=s) + Eh = model(ch["yI"], ch["yQ"], active=act) + loss = loss + semantic_loss(Eh, E, 0.1, active=act.float()) + (loss / len(snrs)).backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0) + opt.step() + if ep % 50 == 0: + print(f" [E3-{tag}] ep {ep}/{epochs} loss={float(loss)/len(snrs):.4f}", + flush=True) + + +@torch.no_grad() +def evaluate(model, k, snr, n_mc=200): + s_acc = c_acc = 0.0 + for _ in range(n_mc): + E = gen(64) + act = sample_active(64, k) + Ez = E * act[:, :, None] + ch = channel(Ez, snr_db=snr) + Eh = model(ch["yI"], ch["yQ"], active=act) + s_acc += ser(Eh, E, active=act.float()) + c_acc += mean_cos(Eh, E, active=act.float()) + return s_acc / n_mc, c_acc / n_mc + + +print("[E3] training activity-aware model", flush=True) +m_act = UWCA(d, Umax, H).to(DEVICE) +train(m_act, tag="act") +torch.save(m_act.state_dict(), lib.DATA / "e3_uwca_act.pt") + +print("[E3] training fixed-U8 model", flush=True) +m_fix = UWCA(d, Umax, H).to(DEVICE) +train(m_fix, k=Umax, tag="fix8") + +ks = [2, 3, 4, 5, 6, 7, 8] +out = {"k": ks, "snr_eval": [10.0, 20.0], "activity": {}, "fixed8": {}, + "oracle": {}} +for snr in out["snr_eval"]: + out["activity"][str(snr)] = [evaluate(m_act, k, snr) for k in ks] + out["fixed8"][str(snr)] = [evaluate(m_fix, k, snr) for k in ks] + print(f"[E3] snr={snr} activity={[f'{a[0]:.3f}' for a in out['activity'][str(snr)]]}", + flush=True) + +for k in [2, 4, 6, 8]: + m_o = UWCA(d, Umax, H).to(DEVICE) + train(m_o, k=k, epochs=250, tag=f"oracle{k}") + out["oracle"][str(k)] = {str(snr): evaluate(m_o, k, snr) + for snr in out["snr_eval"]} + print(f"[E3] oracle k={k}: {out['oracle'][str(k)]}", flush=True) + +save_json("e3_dynamic_users.json", out) diff --git a/rev2/e4_v3_async.py b/rev2/e4_v3_async.py new file mode 100755 index 0000000..8118b7e --- /dev/null +++ b/rev2/e4_v3_async.py @@ -0,0 +1,94 @@ +"""E4 v3 — Timing offsets with BLOCK-WISE receiver realignment (R1.8). + +The BS knows the per-user timing estimates (pilot-based) and realigns each +user's block region individually inside the single received frame: + y_al[b_w + t] = y[b_w + t + delta_w], t = 0..d/U-1 +Energy that crossed block boundaries is lost or appears as residual +interference, exactly as in a real system with per-user timing advance error. +""" +import numpy as np +import torch +import torch.nn.functional as F + +import lib +from lib import (SCENARIOS, UWCA, DEVICE, block_masks, channel, gen_embeddings, + mean_cos, ofdma_decode, save_json, ser, set_seed) + +rng = set_seed(42) +d, U, H = 64, 4, 4 +dpu = d // U +masks = block_masks(U, d) +scen = SCENARIOS["HIGH"] +off_rng = np.random.default_rng(5) + + +def gen(n): + return gen_embeddings(n, d, U, rng, scen).to(DEVICE) + + +model = UWCA(d, U, H).to(DEVICE) +model.load_state_dict(torch.load(lib.DATA / "e1_uwca_HIGH.pt", + map_location=DEVICE)) +model.eval() + + +def realign(y, offs_hat): + """Block-wise realignment: shift each user's block back by its offset.""" + n, dd = y.shape + out = torch.zeros_like(y) + for u in range(U): + b0 = u * dpu + for o in offs_hat[:, u].unique(): + o = int(o.item()) + idx = offs_hat[:, u] == o + src_end = min(b0 + dpu + o, dd) + ln = src_end - (b0 + o) + if ln > 0: + out[idx, b0:b0 + ln] = y[idx, b0 + o:src_end] + return out + + +@torch.no_grad() +def evaluate(mode, dmax, snr, err_p=0.0, n_mc=200): + s_acc = c_acc = 0.0 + for _ in range(n_mc): + E = gen(64) + offs = None + if dmax > 0: + offs = torch.from_numpy( + off_rng.integers(0, dmax + 1, size=(64, U))).to(DEVICE) + ch = channel(E, snr_db=snr, offsets=offs) + y = ch["yI"] + if dmax > 0 and mode.endswith("cor"): + offs_hat = offs.clone() + if err_p > 0: + flip = torch.from_numpy( + off_rng.random((64, U)) < err_p).to(DEVICE) + pm = torch.from_numpy( + off_rng.choice([-1, 1], size=(64, U))).to(DEVICE) + offs_hat = (offs_hat + flip.long() * pm).clamp(min=0) + y = realign(y, offs_hat) + if mode.startswith("uwca"): + Eh = model(y, ch["yQ"]) + else: + Eh = ofdma_decode(y, masks) + s_acc += ser(Eh, E) + c_acc += mean_cos(Eh, E) + return s_acc / n_mc, c_acc / n_mc + + +dgrid = [0, 1, 2, 4, 8] +out = {"dmax": dgrid, "snr_eval": [10.0, 20.0], "curves": {}} +for label, mode, ep in [("uwca_uncorrected", "uwca_unc", 0.0), + ("ofdma_uncorrected", "ofdma_unc", 0.0), + ("uwca_corrected", "uwca_cor", 0.0), + ("ofdma_corrected", "ofdma_cor", 0.0), + ("uwca_corrected_err20", "uwca_cor", 0.2)]: + cur = {} + for snr in out["snr_eval"]: + cur[str(snr)] = [evaluate(mode, dm, snr, ep) for dm in dgrid] + out["curves"][label] = cur + print(f"[E4v3] {label} 10dB SER: " + f"{[round(a[0],3) for a in cur['10.0']]}", flush=True) + +save_json("e4_v3_async.json", out) diff --git a/rev2/e5_nonlinear.py b/rev2/e5_nonlinear.py new file mode 100755 index 0000000..173394f --- /dev/null +++ b/rev2/e5_nonlinear.py @@ -0,0 +1,55 @@ +"""E5 — Nonlinear inter-user semantic structure (R1.7, R2.5, R3.2). + +Embeddings are produced by fixed random per-user nonlinear view networks +e_u = normalize(g_u([kappa*s ; p_u])), so the inter-user dependence is +nonlinear and NOT captured by any scalar coefficient or linear covariance. +NONLIN-HIGH: shared s (kappa=1); NONLIN-LOW: independent s per user. +Shows the trained UWCA decoder still exploits the shared structure while the +scalar-parameterized genie LMMSE (mis-specified here) cannot fully. +""" +import numpy as np +import torch + +import lib +from lib import (UWCA, DEVICE, ViewNets, block_masks, eval_scheme, save_json, + set_seed, train_multitask, SNR_GRID) + +rng = set_seed(42) +d, U, H = 64, 4, 4 +masks = block_masks(U, d) +vnets = ViewNets(d, U).to(DEVICE) +tasks = [{"snr_db": float(s)} for s in np.arange(0, 21, 4)] + +out = {"snr": SNR_GRID.tolist(), "cases": {}} +for case, (kappa, shared) in {"NONLIN-HIGH": (1.0, True), + "NONLIN-LOW": (1.0, False)}.items(): + def gen(n, kappa=kappa, shared=shared): + return vnets.gen(n, d, U, rng, kappa, shared) + + # empirical mean pairwise cosine (the "effective" relevance) + E = gen(2048) + C = torch.einsum("nud,nvd->uv", E, E) / 2048 + off = C[~torch.eye(U, dtype=torch.bool, device=C.device)] + beta_emp = float(off.mean()) + print(f"[E5-{case}] empirical mean pairwise cosine = {beta_emp:.3f}", + flush=True) + + # mis-specified scalar-model LMMSE uses beta_emp for every pair + B = np.full((U, U), beta_emp) + np.fill_diagonal(B, 1.0) + + model = UWCA(d, U, H).to(DEVICE) + train_multitask(model, gen, tasks, epochs=300, tag=f"E5-{case}") + + res = {"beta_emp": beta_emp} + for scheme in ["uwca", "ofdma", "noma", "lmmse_genie"]: + sers, coss = [], [] + for snr in SNR_GRID: + s, c = eval_scheme(scheme, gen, {"snr_db": float(snr)}, n_mc=200, + model=model, B=B, masks=masks) + sers.append(s); coss.append(c) + res[scheme] = {"ser": sers, "cos": coss} + print(f"[E5-{case}] {scheme}: SER@10dB={sers[5]:.3f}", flush=True) + out["cases"][case] = res + +save_json("e5_nonlinear.json", out) diff --git a/rev2/e6_residual_orth.py b/rev2/e6_residual_orth.py new file mode 100755 index 0000000..62006fe --- /dev/null +++ b/rev2/e6_residual_orth.py @@ -0,0 +1,66 @@ +"""E6 — Residual (error) orthogonality vs. content preservation (R1.4, R3.3, R2.2). + +Resolves the claimed contradiction: the decoded embeddings PRESERVE the shared +scene correlation (rho(e_hat_u, e_hat_v) tracks beta_uv), while the decoding +RESIDUALS r_u = e_hat_u - e_u decorrelate (rho(r_u, r_v) -> 0), which is the +interference-suppression property. Measured per-sample across dimensions on +the E1 HIGH-trained decoder, vs SNR, together with the scene-component cosine. +""" +import numpy as np +import torch + +import lib +from lib import (SCENARIOS, SNR_GRID, UWCA, DEVICE, beta_matrix, block_masks, + channel, gen_embeddings, ofdma_decode, sample_corr, save_json, + set_seed) + +rng = set_seed(42) +d, U, H = 64, 4, 4 +masks = block_masks(U, d) +scen = SCENARIOS["HIGH"] +B = beta_matrix(scen) + +model = UWCA(d, U, H).to(DEVICE) +model.load_state_dict(torch.load(lib.DATA / "e1_uwca_HIGH.pt", + map_location=DEVICE)) +model.eval() + +pairs = [(u, v) for u in range(U) for v in range(u + 1, U)] +out = {"snr": SNR_GRID.tolist(), "beta_uv_mean": float(np.mean( + [B[u, v] for u, v in pairs])), "uwca": {}, "ofdma": {}} + + +@torch.no_grad() +def measure(decode_fn): + rho_in, rho_out, rho_res = [], [], [] + for snr in SNR_GRID: + a_in = a_out = a_res = 0.0 + n_mc = 100 + for _ in range(n_mc): + E = gen_embeddings(64, d, U, rng, scen).to(DEVICE) + ch = channel(E, snr_db=float(snr)) + Eh = decode_fn(ch) + R = Eh - E + pi = po = pr = 0.0 + for u, v in pairs: + pi += sample_corr(E[:, u], E[:, v]) + po += sample_corr(Eh[:, u], Eh[:, v]) + pr += sample_corr(R[:, u], R[:, v]) + a_in += pi / len(pairs) + a_out += po / len(pairs) + a_res += pr / len(pairs) + rho_in.append(a_in / n_mc) + rho_out.append(a_out / n_mc) + rho_res.append(a_res / n_mc) + return rho_in, rho_out, rho_res + + +ri, ro, rr = measure(lambda ch: model(ch["yI"], ch["yQ"])) +out["uwca"] = {"rho_input": ri, "rho_decoded": ro, "rho_residual": rr} +print(f"[E6] UWCA rho_in={ri[5]:.3f} rho_dec={ro[5]:.3f} rho_res={rr[5]:.3f} @10dB", + flush=True) + +ri, ro, rr = measure(lambda ch: ofdma_decode(ch["yI"], masks)) +out["ofdma"] = {"rho_input": ri, "rho_decoded": ro, "rho_residual": rr} + +save_json("e6_residual_orth.json", out) diff --git a/rev2/e7_v2_meta.py b/rev2/e7_v2_meta.py new file mode 100755 index 0000000..b34b732 --- /dev/null +++ b/rev2/e7_v2_meta.py @@ -0,0 +1,93 @@ +"""E7 v2 — Meta-training over the multi-dimensional task family, OOD transfer, +adaptation sweep, and eta/gradient logging. + +meta : the paper's first-order meta-training aggregated over the FULL + 36-task family (SNR x {Rayleigh, Rician K=5,10 dB} x phase {0,10 deg}) +lookup : per-SNR specialists trained on Rayleigh / no phase error, indexed by + nearest SNR (the 1-D lookup table) +Test on held-out task combinations (incl. unseen Nakagami fading), zero-shot +and with S in {1,5,10,20} inner adaptation steps from each initialization. +""" +import numpy as np +import torch + +import lib +from lib import (SCENARIOS, UWCA, DEVICE, adapt, block_masks, eval_scheme, + gen_embeddings, save_json, set_seed, train_multitask) + +rng = set_seed(42) +d, U, H = 64, 4, 4 +masks = block_masks(U, d) +scen = SCENARIOS["HIGH"] + + +def gen(n): + return gen_embeddings(n, d, U, rng, scen).to(DEVICE) + + +snrs = [0.0, 4.0, 8.0, 12.0, 16.0, 20.0] +fads = [{"fading": "rayleigh"}, + {"fading": "rician", "rician_K_dB": 5.0}, + {"fading": "rician", "rician_K_dB": 10.0}] +phis = [0.0, 10.0] +family = [{"snr_db": s, "phase_sigma_deg": p, **f} + for s in snrs for f in fads for p in phis] + +eta_log = [] +m_meta = UWCA(d, U, H).to(DEVICE) +train_multitask(m_meta, gen, family, epochs=300, tag="E7v2-meta", + log_state=eta_log) +torch.save(m_meta.state_dict(), lib.DATA / "e7v2_meta.pt") + +specialists = {} +for s in snrs: + m = UWCA(d, U, H).to(DEVICE) + train_multitask(m, gen, [{"snr_db": s}], epochs=150, + tag=f"E7v2-spec{int(s)}") + specialists[s] = m + + +def lookup(snr): + return specialists[min(snrs, key=lambda x: abs(x - snr))] + + +test_tasks = { + "ricianK20_phi15_snr10": {"snr_db": 10.0, "fading": "rician", + "rician_K_dB": 20.0, "phase_sigma_deg": 15.0}, + "nakagami3_phi5_snr10": {"snr_db": 10.0, "fading": "nakagami", + "nakagami_m": 3.0, "phase_sigma_deg": 5.0}, + "rayleigh_phi20_snr6": {"snr_db": 6.0, "fading": "rayleigh", + "phase_sigma_deg": 20.0}, + "ricianK20_phi15_snr18": {"snr_db": 18.0, "fading": "rician", + "rician_K_dB": 20.0, "phase_sigma_deg": 15.0}, + "indist_rayleigh_snr10": {"snr_db": 10.0, "fading": "rayleigh", + "phase_sigma_deg": 0.0}, +} + +S_grid = [0, 1, 5, 10, 20] +out = {"S_grid": S_grid, "results": {}} +for name, t in test_tasks.items(): + row = {} + for label, base in [("meta", m_meta), ("lookup", lookup(t["snr_db"]))]: + sers = [] + for S in S_grid: + mdl = base if S == 0 else adapt(base, gen, t, steps=S, + inner_lr=0.02) + s, c = eval_scheme("uwca", gen, t, n_mc=150, model=mdl, + masks=masks) + sers.append({"ser": s, "cos": c}) + row[label] = sers + out["results"][name] = row + print(f"[E7v2] {name}: meta={[round(x['ser'],3) for x in row['meta']]} " + f"lookup={[round(x['ser'],3) for x in row['lookup']]}", flush=True) + +etas = [e["eta"] for e in eta_log] +gns = [e["gnorm"] for e in eta_log] +out["eta_traj"] = etas[::5] +out["gnorm_traj"] = gns[::5] +out["eta_final"], out["eta_max"] = etas[-1], max(etas) +out["gnorm_max"] = max(gns) +print(f"[E7v2] eta final={etas[-1]:.3f} max={max(etas):.3f} " + f"gnorm max={max(gns):.3f}", flush=True) + +save_json("e7_v2_meta.json", out) diff --git a/rev2/e8_v2_e2e.py b/rev2/e8_v2_e2e.py new file mode 100755 index 0000000..fd49bb7 --- /dev/null +++ b/rev2/e8_v2_e2e.py @@ -0,0 +1,125 @@ +"""E8 v2 — End-to-end joint encoder-decoder training (R1.5), source-anchored. + +All fidelity metrics are measured against the SOURCE embedding normalize(x), +never against the trainable encoder output (a moving target that makes +collapse look like success). + +Configs (HIGH): + frozen : identity encoder, decoder trained (paper reference) + e2e_moving : trainable encoder, loss vs f_phi(x) (collapse demo) + e2e_anchored : trainable encoder, loss vs normalize(x) (fixed anchor) + e2e_vicreg : trainable encoder, loss vs f_phi(x) + VICReg anti-collapse +Metrics: SER/cos vs normalize(x); batch nearest-neighbor retrieval accuracy; +encoder-output effective rank and off-diagonal correlation. +""" +import numpy as np +import torch +import torch.nn.functional as F + +import lib +from lib import (SCENARIOS, SNR_GRID, UWCA, DEVICE, Encoder, block_masks, + channel, gen_embeddings, save_json, semantic_loss, set_seed) + +rng = set_seed(42) +d, U, H = 64, 4, 4 +masks = block_masks(U, d) +scen = SCENARIOS["HIGH"] +snrs = [0.0, 4.0, 8.0, 12.0, 16.0, 20.0] + + +def gen(n): + return gen_embeddings(n, d, U, rng, scen).to(DEVICE) + + +def vicreg_reg(Z): + Zc = Z - Z.mean(0, keepdim=True) + std = (Zc.var(0) + 1e-4).sqrt() + v = F.relu(1.0 / d ** 0.5 - std).mean() + C = (Zc.T @ Zc) / (Z.shape[0] - 1) + off = C - torch.diag(torch.diag(C)) + c = (off ** 2).sum() / d + return 25.0 * v + 100.0 * c + + +def train(mode, epochs=300): + model = UWCA(d, U, H).to(DEVICE) + enc = Encoder(d).to(DEVICE) if mode != "frozen" else None + mask_p = [p for nm, p in model.named_parameters() if "mask_logits" in nm] + other = [p for nm, p in model.named_parameters() if "mask_logits" not in nm] + groups = [{"params": other, "lr": 1e-3}, {"params": mask_p, "lr": 0.1}] + if enc is not None: + groups.append({"params": enc.parameters(), "lr": 1e-3}) + opt = torch.optim.Adam(groups) + for ep in range(1, epochs + 1): + opt.zero_grad() + loss = 0.0 + for s in snrs: + X = gen(64) + n = X.shape[0] + E = enc(X.reshape(-1, d)).reshape(n, U, d) if enc is not None else X + target = E if mode in ("frozen", "e2e_moving", "e2e_vicreg") else X + ch = channel(E, snr_db=s) + Eh = model(ch["yI"], ch["yQ"]) + L = semantic_loss(Eh, target.detach() + if mode == "e2e_moving_detach" else target, 0.1) + if mode == "e2e_vicreg": + L = L + vicreg_reg(E.reshape(-1, d)) + loss = loss + L + (loss / len(snrs)).backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0) + opt.step() + if ep % 75 == 0: + print(f" [E8v2-{mode}] ep {ep}/{epochs} " + f"loss={float(loss)/len(snrs):.4f}", flush=True) + return model, enc + + +@torch.no_grad() +def collapse_metrics(enc): + X = gen(1024).reshape(-1, d) + Z = enc(X) if enc is not None else F.normalize(X, dim=-1) + Zc = Z - Z.mean(0, keepdim=True) + C = (Zc.T @ Zc) / (Z.shape[0] - 1) + ev = torch.linalg.eigvalsh(C).clamp(min=1e-12) + p = ev / ev.sum() + return float(torch.exp(-(p * p.log()).sum())) + + +@torch.no_grad() +def curves(model, enc): + ss, cc, rr = [], [], [] + for snr in SNR_GRID: + a = b = r = 0.0 + n_mc = 150 + for _ in range(n_mc): + X = gen(64) + n = X.shape[0] + E = enc(X.reshape(-1, d)).reshape(n, U, d) if enc is not None else X + ch = channel(E, snr_db=float(snr)) + Eh = model(ch["yI"], ch["yQ"]) + # all metrics vs the SOURCE + cos = (Eh * X).sum(-1) + a += float((cos < 0.45).float().mean()) + b += float(cos.mean()) + # batch retrieval: nearest ENCODED gallery entry (collapse makes + # the gallery indistinguishable and drives accuracy to chance) + q = Eh.reshape(-1, d) + g = E.reshape(-1, d) + sim = q @ g.T + r += float((sim.argmax(1) == torch.arange(q.shape[0], + device=q.device)) + .float().mean()) + ss.append(a / n_mc); cc.append(b / n_mc); rr.append(r / n_mc) + return ss, cc, rr + + +out = {"snr": SNR_GRID.tolist(), "configs": {}} +for mode in ["frozen", "e2e_moving", "e2e_anchored", "e2e_vicreg"]: + model, enc = train(mode) + erank = collapse_metrics(enc) + ss, cc, rr = curves(model, enc) + out["configs"][mode] = {"ser": ss, "cos": cc, "retr": rr, "erank": erank} + print(f"[E8v2] {mode}: erank={erank:.1f} SER@10={ss[5]:.3f} " + f"cos@10={cc[5]:.3f} retr@10={rr[5]:.3f}", flush=True) + +save_json("e8_v2_e2e.json", out) diff --git a/rev2/e9_topk_online.py b/rev2/e9_topk_online.py new file mode 100755 index 0000000..eb736f7 --- /dev/null +++ b/rev2/e9_topk_online.py @@ -0,0 +1,144 @@ +"""E9 — Online relevance acquisition for sparse top-k attention (R1.2, R3.6) +and measured selection/sorting overhead (R2.4). + +Protocol (U=32, 8 clusters of 4, k=4): + frames 1..3 : full attention; the BS estimates beta_hat from the decoded + embeddings by an EWMA of pairwise cosines (no oracle knowledge) + frames >=4 : top-k attention using beta_hat (self + k-1 best peers) +Reports the per-frame fidelity trajectory against the oracle top-k (true +clusters) and full attention, plus wall-clock timing of the full pipeline +including estimation and argpartition selection for U in {8..128}. +""" +import time + +import numpy as np +import torch + +import lib +from lib import (UWCA, DEVICE, block_masks, channel, gen_embeddings, + mean_cos, save_json, semantic_loss, ser, set_seed) + +rng = set_seed(42) +d, H, k = 64, 4, 4 +U = 32 +G = U // k # 8 clusters of 4 +scen = {"beta_u": [0.65] * U, "scenes": [i // k for i in range(U)]} +masks = block_masks(U, d) +snrs = [0.0, 10.0, 20.0] + + +def gen(n): + return gen_embeddings(n, d, U, rng, scen).to(DEVICE) + + +def cluster_mask(): + m = torch.zeros(U, U, dtype=torch.bool, device=DEVICE) + for u in range(U): + c = u // k + m[u, c * k:(c + 1) * k] = True + return m + + +def topk_from_beta(bhat): + m = torch.zeros(U, U, dtype=torch.bool, device=DEVICE) + b = bhat.clone() + b.fill_diagonal_(2.0) # always keep self + idx = torch.topk(b, k, dim=1).indices + m.scatter_(1, idx, True) + return m + + +print("[E9] training U=32 model (full attention)", flush=True) +model = UWCA(d, U, H).to(DEVICE) +mask_p = [p for nm, p in model.named_parameters() if "mask_logits" in nm] +other = [p for nm, p in model.named_parameters() if "mask_logits" not in nm] +opt = torch.optim.Adam([{"params": other, "lr": 1e-3}, + {"params": mask_p, "lr": 0.1}]) +for ep in range(1, 181): + opt.zero_grad() + loss = 0.0 + for s in snrs: + E = gen(64) + ch = channel(E, snr_db=s) + Eh = model(ch["yI"], ch["yQ"]) + loss = loss + semantic_loss(Eh, E, 0.1) + (loss / len(snrs)).backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0) + opt.step() + if ep % 45 == 0: + print(f" [E9] ep {ep}/180 loss={float(loss)/len(snrs):.4f}", flush=True) + +# ---- online protocol trajectory at 10 dB +T, warm, gamma_ewma = 16, 3, 0.5 +oracle = cluster_mask() +traj = {"frame": list(range(1, T + 1)), "online_cos": [], "oracle_cos": [], + "full_cos": [], "beta_err": []} +bhat = torch.zeros(U, U, device=DEVICE) +model.eval() +Btrue = torch.zeros(U, U, device=DEVICE) +for u in range(U): + for v in range(U): + if u != v and scen["scenes"][u] == scen["scenes"][v]: + Btrue[u, v] = scen["beta_u"][u] * scen["beta_u"][v] + +with torch.no_grad(): + for t in range(1, T + 1): + E = gen(256) + ch = channel(E, snr_db=10.0) + tk = None if t <= warm else topk_from_beta(bhat) + Eh = model(ch["yI"], ch["yQ"], topk_mask=tk) + # BS-side estimate from decoded embeddings only + Cb = torch.einsum("nud,nvd->uv", Eh, Eh) / Eh.shape[0] + Cb.fill_diagonal_(0.0) + bhat = gamma_ewma * bhat + (1 - gamma_ewma) * Cb + Ehf = model(ch["yI"], ch["yQ"]) + Eho = model(ch["yI"], ch["yQ"], topk_mask=oracle) + traj["online_cos"].append(mean_cos(Eh, E)) + traj["full_cos"].append(mean_cos(Ehf, E)) + traj["oracle_cos"].append(mean_cos(Eho, E)) + traj["beta_err"].append(float((bhat - Btrue).abs().mean())) + print(f"[E9] frame {t}: online={traj['online_cos'][-1]:.4f} " + f"oracle={traj['oracle_cos'][-1]:.4f} " + f"full={traj['full_cos'][-1]:.4f}", flush=True) + +# ---- wall-clock overhead incl. estimation + argpartition selection +timing = {"U": [8, 16, 32, 64, 128], "full_ms": [], "topk_ms": [], + "select_ms": []} +for Ut in timing["U"]: + dt = max(d, 2 * Ut) # keep at least 2 dims per user slot + mt = UWCA(dt, Ut, H).to(DEVICE).eval() + ch = {"yI": torch.randn(256, dt, device=DEVICE), + "yQ": torch.randn(256, dt, device=DEVICE)} + bh = torch.rand(Ut, Ut, device=DEVICE) + with torch.no_grad(): + for _ in range(3): + mt(ch["yI"], ch["yQ"]) # warm-up + torch.cuda.synchronize() + t0 = time.perf_counter() + for _ in range(20): + mt(ch["yI"], ch["yQ"]) + torch.cuda.synchronize() + t_full = (time.perf_counter() - t0) / 20 * 1e3 + t0 = time.perf_counter() + for _ in range(20): + b = bh.clone(); b.fill_diagonal_(2.0) + idx = torch.topk(b, k, dim=1).indices + tkm = torch.zeros(Ut, Ut, dtype=torch.bool, device=DEVICE) + tkm.scatter_(1, idx, True) + mt(ch["yI"], ch["yQ"], topk_mask=tkm) + torch.cuda.synchronize() + t_topk = (time.perf_counter() - t0) / 20 * 1e3 + t0 = time.perf_counter() + for _ in range(100): + b = bh.clone(); b.fill_diagonal_(2.0) + idx = torch.topk(b, k, dim=1).indices + torch.cuda.synchronize() + t_sel = (time.perf_counter() - t0) / 100 * 1e3 + timing["full_ms"].append(t_full) + timing["topk_ms"].append(t_topk) + timing["select_ms"].append(t_sel) + print(f"[E9] U={Ut}: full={t_full:.2f}ms topk={t_topk:.2f}ms " + f"select={t_sel:.3f}ms", flush=True) + +save_json("e9_topk_online.json", {"trajectory": traj, "timing": timing, + "k": k, "warm_frames": warm}) diff --git a/rev2/lib.py b/rev2/lib.py new file mode 100755 index 0000000..69092f1 --- /dev/null +++ b/rev2/lib.py @@ -0,0 +1,490 @@ +""" +Shared library for TWC revision-2 experiments (new submission). +Single-signal uplink model matching the manuscript: + y = sum_v g_v (e_v ⊙ m_v) + n, g_v = |h_v| e^{jΔφ_v} +All experiments import from here. Seed fixed = 42. +""" +import copy +import json +import math +import time +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") +DATA = Path(__file__).resolve().parent / "data" +DATA.mkdir(exist_ok=True) + +SCENARIOS = { + "HIGH": {"beta_u": [0.65, 0.65, 0.60, 0.60], "scenes": [0, 0, 0, 0]}, + "LOW": {"beta_u": [0.65, 0.05, 0.05, 0.05], "scenes": [0, 1, 2, 3]}, + "MIX": {"beta_u": [0.65, 0.65, 0.05, 0.05], "scenes": [0, 0, 1, 2]}, + # 8-slot high-correlation scenario for dynamic-user experiment + "HIGH8": {"beta_u": [0.60] * 8, "scenes": [0] * 8}, +} + +SNR_GRID = np.arange(0.0, 20.0 + 1e-6, 2.0) + + +def beta_matrix(scen): + """True relevance matrix beta_{u,v} = beta_u beta_v [same scene], else 0.""" + b = np.asarray(scen["beta_u"], dtype=np.float64) + sc = np.asarray(scen["scenes"]) + U = len(b) + B = np.zeros((U, U)) + for u in range(U): + for v in range(U): + B[u, v] = 1.0 if u == v else (b[u] * b[v] if sc[u] == sc[v] else 0.0) + return B + + +def gen_embeddings(n, d, U, rng, scen): + """Unit-norm ground-truth embeddings (n, U, d): e_u = sqrt(1-b^2) p + b s.""" + blend, scenes = scen["beta_u"], scen["scenes"] + svecs = {} + for sc in sorted(set(scenes)): + v = rng.standard_normal(d) + svecs[sc] = v / (np.linalg.norm(v) + 1e-8) + embs = [] + for u in range(U): + b = blend[u] + p = rng.standard_normal((n, d)) + p /= np.linalg.norm(p, axis=-1, keepdims=True) + 1e-8 + e = np.sqrt(max(1 - b * b, 0.0)) * p + b * svecs[scenes[u]][None, :] + e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8 + embs.append(e) + return torch.from_numpy(np.stack(embs, 1)).float() + + +class ViewNets(nn.Module): + """Fixed random per-user nonlinear view functions g_u([kappa*s; p_u]).""" + + def __init__(self, d, U, seed=7): + super().__init__() + g = torch.Generator().manual_seed(seed) + self.nets = nn.ModuleList() + for _ in range(U): + l1 = nn.Linear(2 * d, 2 * d) + l2 = nn.Linear(2 * d, d) + for l in (l1, l2): + nn.init.normal_(l.weight, std=(2.0 / l.in_features) ** 0.5, generator=g) + nn.init.zeros_(l.bias) + self.nets.append(nn.Sequential(l1, nn.Tanh(), l2)) + for p in self.parameters(): + p.requires_grad_(False) + + @torch.no_grad() + def gen(self, n, d, U, rng, kappa, shared_scene=True): + if shared_scene: + s = rng.standard_normal((n, d)) / math.sqrt(d) + s = np.repeat(s[:, None, :], U, axis=1) + else: + s = rng.standard_normal((n, U, d)) / math.sqrt(d) + p = rng.standard_normal((n, U, d)) / math.sqrt(d) + s = torch.from_numpy(s).float().to(DEVICE) + p = torch.from_numpy(p).float().to(DEVICE) + outs = [] + for u in range(U): + x = torch.cat([kappa * s[:, u], p[:, u]], -1) + outs.append(F.normalize(self.nets[u](x), dim=-1)) + return torch.stack(outs, 1) # (n, U, d) + + +def block_masks(U, d, device=DEVICE): + dpu = d // U + m = torch.zeros(U, d, device=device) + for u in range(U): + m[u, u * dpu:(u + 1) * dpu] = 1.0 + return m + + +def channel(E, snr_db, *, phase_sigma_deg=0.0, fading="rayleigh", rician_K_dB=None, + nakagami_m=None, offsets=None, h_err_sigma=0.0, masks=None, use_masks=True): + """Single-signal uplink. Returns dict with yI, yQ, h (true magnitude), h_hat. + + E : (n, U, d) ground-truth embeddings on DEVICE. + offsets : (n, U) integer per-user timing offsets (symbols), or None. + Noise convention: per-rail noise std = sqrt(mean|y_tx|^2 / snr_lin); with + phase_sigma = 0 the model reduces exactly to the real-valued model. + """ + n, U, d = E.shape + if masks is None: + masks = block_masks(U, d, E.device) + X = E * masks[None] if use_masks else E.clone() + + if offsets is not None: + Xs = torch.zeros_like(X) + offs = offsets + for u in range(U): + for o in offs[:, u].unique(): + o = int(o.item()) + idx = offs[:, u] == o + if o == 0: + Xs[idx, u] = X[idx, u] + else: + Xs[idx, u, o:] = X[idx, u, :d - o] + X = Xs + + if fading == "rayleigh": + hI = torch.randn(n, U, device=E.device) * (0.5 ** 0.5) + hQ = torch.randn(n, U, device=E.device) * (0.5 ** 0.5) + hmag = (hI ** 2 + hQ ** 2).sqrt() + elif fading == "rician": + K = 10 ** (rician_K_dB / 10.0) + mu = math.sqrt(K / (K + 1)) + sig = math.sqrt(1.0 / (2 * (K + 1))) + hI = mu + torch.randn(n, U, device=E.device) * sig + hQ = torch.randn(n, U, device=E.device) * sig + hmag = (hI ** 2 + hQ ** 2).sqrt() + elif fading == "nakagami": + m = nakagami_m + gam = torch.distributions.Gamma(m, m).sample((n, U)).to(E.device) + hmag = gam.sqrt() + else: + raise ValueError(fading) + + dphi = torch.randn(n, U, device=E.device) * math.radians(phase_sigma_deg) + gI = hmag * torch.cos(dphi) + gQ = hmag * torch.sin(dphi) + + yI = (gI[:, :, None] * X).sum(1) + yQ = (gQ[:, :, None] * X).sum(1) + P = (yI ** 2 + yQ ** 2).mean() + nstd = (P / (10 ** (snr_db / 10.0))).sqrt() + yI = yI + torch.randn_like(yI) * nstd + yQ = yQ + torch.randn_like(yQ) * nstd + h_hat = hmag * (1 + torch.randn_like(hmag) * h_err_sigma) if h_err_sigma > 0 else hmag + return {"yI": yI, "yQ": yQ, "h": hmag, "h_hat": h_hat, "nvar": float(nstd ** 2)} + + +# ---------------------------------------------------------------- decoders -- +class UWCA(nn.Module): + """User-wise cross-attention decoder on the single superimposed signal. + + iq=True : keys/values read the stacked [I; Q] rails (2d input). + forward(yI, yQ, active) with active (n, U) bool or None. + """ + + def __init__(self, d, U, H=4, iq=False): + super().__init__() + assert d % H == 0 + self.d, self.U, self.H, self.dk = d, U, H, d // H + self.iq = iq + din = 2 * d if iq else d + self.q_vectors = nn.Parameter(torch.randn(U, d) * d ** -0.5) + self.eta = nn.Parameter(torch.ones(1)) + self.W_K = nn.Linear(din, d, bias=False) + self.W_V = nn.Linear(din, d, bias=False) + self.W_O = nn.Linear(d, d, bias=False) + self.norm = nn.LayerNorm(d) + init_logits = torch.full((U, d), -3.0) + dpu = d // U + for u in range(U): + init_logits[u, u * dpu:(u + 1) * dpu] = 3.0 + self.mask_logits = nn.Parameter(init_logits) + + def soft_masks(self): + return torch.sigmoid(self.mask_logits) + + def forward(self, yI, yQ=None, active=None, topk_mask=None, return_alpha=False): + # yI: (n, d) shared signal, or (n, U, d) per-candidate aligned copies + U, H, dk = self.U, self.H, self.dk + m = self.soft_masks() # (U, d) + if yI.dim() == 2: + n, d = yI.shape + yIc = yI[:, None, :].expand(-1, U, -1) + else: + n, _, d = yI.shape + yIc = yI + R = yIc * m[None] # (n, U, d) + if self.iq: + yQc = yQ[:, None, :].expand(-1, U, -1) if yQ.dim() == 2 else yQ + RQ = yQc * m[None] + Rin = torch.cat([R, RQ], -1) # (n, U, 2d) + else: + Rin = R + K = self.W_K(Rin).view(n, U, H, dk) # (n, Uk, H, dk) + V = self.W_V(Rin).view(n, U, H, dk) + Q = self.q_vectors.view(U, H, dk) # (Uq, H, dk) + scores = torch.einsum("qhk,nihk->nqhi", Q, K) * self.eta / dk ** 0.5 + if active is not None: # (n, U) bool + scores = scores.masked_fill(~active[:, None, None, :], -1e9) + if topk_mask is not None: # (U, U) bool keep + scores = scores.masked_fill(~topk_mask[None, :, None, :], -1e9) + alpha = F.softmax(scores, dim=-1) # (n, Uq, H, Uk) + ctx = torch.einsum("nqhi,nihk->nqhk", alpha, V).reshape(n, U, d) + own = yIc * m[None] + out = F.normalize(self.norm(self.W_O(ctx) + own), dim=-1) + if return_alpha: + return out, alpha.mean(dim=(0, 2)) + return out + + +class Encoder(nn.Module): + """Trainable semantic encoder for the end-to-end experiment.""" + + def __init__(self, d): + super().__init__() + self.net = nn.Sequential(nn.Linear(d, 2 * d), nn.LayerNorm(2 * d), + nn.GELU(), nn.Linear(2 * d, d)) + + def forward(self, x): + return F.normalize(self.net(x), dim=-1) + + +def ofdma_decode(yI, masks): + return F.normalize(yI[:, None, :] * masks[None], dim=-1) + + +def tdma_proj_decode(E, snr_db, rng_t): + """Orthogonal scheme with an arbitrary (random orthonormal) d/U-dim + projection per user instead of coordinate masks: z_u = h_u P_u e_u + n.""" + n, U, d = E.shape + dpu = d // U + Q, _ = torch.linalg.qr(torch.randn(d, d, generator=rng_t).to(E.device)) + outs = [] + hmag = (torch.randn(n, U, device=E.device) ** 2 + + torch.randn(n, U, device=E.device) ** 2).sqrt() * 0.5 ** 0.5 + snr_lin = 10 ** (snr_db / 10.0) + for u in range(U): + P = Q[u * dpu:(u + 1) * dpu] # (dpu, d) + z = hmag[:, u:u + 1] * (E[:, u] @ P.T) # (n, dpu) + nstd = (z.pow(2).mean() / snr_lin).sqrt() + z = z + torch.randn_like(z) * nstd + outs.append(F.normalize(z @ P, dim=-1)) + return torch.stack(outs, 1) + + +def lmmse_decode(yI, h, nvar, B, masks, genie=True): + """Closed-form linear MMSE on the block model. C_vv = I/d, C_uv = B_uv I/d. + Block-diagonal C_y => per-block Wiener weights w_uv = h_v B_uv/d / (h_v^2/d + nvar). + genie=False zeroes the cross terms (correlation-blind).""" + n, d = yI.shape + U = masks.shape[0] + Bm = torch.as_tensor(B, dtype=torch.float32, device=yI.device) + if not genie: + Bm = torch.eye(U, device=yI.device) + yb = yI[:, None, :] * masks[None] # (n, Uv, d) block pieces + w = (h[:, None, :] * Bm[None] / d) / (h[:, None, :] ** 2 / d + nvar) # (n,Uu,Uv) + est = torch.einsum("nuv,nvd->nud", w, yb) + return F.normalize(est, dim=-1) + + +def noma_sic_decode(E, snr_db): + """Full-band power-domain NOMA with SIC (no masks).""" + n, U, d = E.shape + pa = torch.tensor([0.40, 0.30, 0.20, 0.10], device=E.device)[:U] + pa = pa / pa.sum() + h = (torch.randn(n, U, 1, device=E.device) ** 2 + + torch.randn(n, U, 1, device=E.device) ** 2).sqrt() * 0.5 ** 0.5 + y = (E * pa.sqrt()[None, :, None] * h).sum(1) + nstd = (y.pow(2).mean() / 10 ** (snr_db / 10.0)).sqrt() + y = y + torch.randn(n, d, device=E.device) * nstd + order = torch.argsort(pa, descending=True) + res = y.clone() + out = torch.zeros_like(E) + for ui in order: + u = int(ui.item()) + eh = F.normalize(res / (h[:, u] + 1e-8), dim=-1) + out[:, u] = eh + res = res - h[:, u] * pa[u].sqrt() * eh + return out + + +# ------------------------------------------------------------------ losses -- +def semantic_loss(Ehat, E, lam=0.1, active=None): + cos = (Ehat * E).sum(-1) + if active is not None: + distortion = ((1 - cos) * active).sum() / active.sum() + else: + distortion = (1 - cos).mean() + U = Ehat.shape[1] + emb = Ehat.mean(0) + ec = emb - emb.mean(1, keepdim=True) + en = F.normalize(ec, dim=1) + C = en @ en.T + off = C[~torch.eye(U, dtype=torch.bool, device=Ehat.device)].abs().mean() + return distortion + lam * off + + +def ser(Ehat, E, tau=0.45, active=None): + bad = ((Ehat * E).sum(-1) < tau).float() + if active is not None: + return float((bad * active).sum() / active.sum()) + return float(bad.mean()) + + +def mean_cos(Ehat, E, active=None): + c = (Ehat * E).sum(-1) + if active is not None: + return float((c * active).sum() / active.sum()) + return float(c.mean()) + + +def sample_corr(A, Bt): + """Mean per-sample Pearson correlation across the d dims of two (n,d) tensors.""" + Ac = A - A.mean(-1, keepdim=True) + Bc = Bt - Bt.mean(-1, keepdim=True) + num = (Ac * Bc).sum(-1) + den = Ac.norm(dim=-1) * Bc.norm(dim=-1) + 1e-9 + return float((num / den).mean()) + + +# ----------------------------------------------------------------- training -- +def train_multitask(model, gen_fn, tasks, epochs=300, batch=64, lam=0.1, + outer_lr=1e-3, mask_lr_mult=100.0, log_every=50, tag="", + encoder=None, extra_loss=None, log_state=None): + """Multi-SNR/-condition aggregated training (the manuscript's outer objective + without inner adaptation). gen_fn(batch) -> E ground truth on DEVICE. + tasks: list of dicts of channel kwargs incl. 'snr_db'.""" + params = [] + mask_p = [p for nm, p in model.named_parameters() if "mask_logits" in nm] + other = [p for nm, p in model.named_parameters() if "mask_logits" not in nm] + params = [{"params": other, "lr": outer_lr}, + {"params": mask_p, "lr": outer_lr * mask_lr_mult}] + if encoder is not None: + params.append({"params": encoder.parameters(), "lr": outer_lr}) + opt = torch.optim.Adam(params) + hist = [] + for ep in range(1, epochs + 1): + loss_acc = 0.0 + opt.zero_grad() + for t in tasks: + E = gen_fn(batch) + if encoder is not None: + n, U, d = E.shape + E = encoder(E.reshape(-1, d)).reshape(n, U, d) + ch = channel(E, **t) + Eh = model(ch["yI"], ch["yQ"]) + L = semantic_loss(Eh, E, lam) + if extra_loss is not None: + L = L + extra_loss(E) + loss_acc += L + (loss_acc / len(tasks)).backward() + nn.utils.clip_grad_norm_(model.parameters(), 5.0) + opt.step() + hist.append(float(loss_acc) / len(tasks)) + if log_state is not None: + gn = sum(float(p.grad.norm()) ** 2 for p in model.parameters() + if p.grad is not None) ** 0.5 + log_state.append({"ep": ep, "eta": float(model.eta), "gnorm": gn}) + if ep % log_every == 0: + print(f" [{tag}] ep {ep}/{epochs} loss={hist[-1]:.4f}", flush=True) + return hist + + +def fomaml_train(model, gen_fn, tasks, epochs=200, batch=64, lam=0.1, + inner_lr=0.01, inner_steps=5, outer_lr=1e-3, + tasks_per_step=8, log_every=25, tag="fomaml", log_state=None, + rng=None): + """Proper first-order MAML: inner SGD on support, outer update from query + gradients evaluated at the adapted parameters.""" + opt = torch.optim.Adam(model.parameters(), lr=outer_lr) + rng = rng or np.random.default_rng(0) + hist = [] + names = [nm for nm, _ in model.named_parameters()] + for ep in range(1, epochs + 1): + idx = rng.choice(len(tasks), size=min(tasks_per_step, len(tasks)), + replace=False) + grads = {nm: torch.zeros_like(p) for nm, p in model.named_parameters()} + qloss_acc = 0.0 + for ti in idx: + t = tasks[ti] + adapted = copy.deepcopy(model) + iopt = torch.optim.SGD(adapted.parameters(), lr=inner_lr) + for _ in range(inner_steps): + E = gen_fn(batch) + ch = channel(E, **t) + L = semantic_loss(adapted(ch["yI"], ch["yQ"]), E, lam) + iopt.zero_grad(); L.backward(); iopt.step() + E = gen_fn(batch) + ch = channel(E, **t) + qL = semantic_loss(adapted(ch["yI"], ch["yQ"]), E, lam) + adapted.zero_grad(); qL.backward() + for nm, p in adapted.named_parameters(): + if p.grad is not None: + grads[nm] += p.grad + qloss_acc += float(qL) + opt.zero_grad() + for nm, p in model.named_parameters(): + p.grad = grads[nm] / len(idx) + nn.utils.clip_grad_norm_(model.parameters(), 5.0) + opt.step() + hist.append(qloss_acc / len(idx)) + if log_state is not None: + gn = sum(float(g.norm()) ** 2 for g in grads.values()) ** 0.5 / len(idx) + log_state.append({"ep": ep, "eta": float(model.eta), "gnorm": gn}) + if ep % log_every == 0: + print(f" [{tag}] ep {ep}/{epochs} qloss={hist[-1]:.4f} " + f"eta={float(model.eta):.3f}", flush=True) + return hist + + +def adapt(model, gen_fn, task, steps=5, inner_lr=0.01, batch=64, lam=0.1): + adapted = copy.deepcopy(model) + iopt = torch.optim.SGD(adapted.parameters(), lr=inner_lr) + for _ in range(steps): + E = gen_fn(batch) + ch = channel(E, **task) + L = semantic_loss(adapted(ch["yI"], ch["yQ"]), E, lam) + iopt.zero_grad(); L.backward(); iopt.step() + return adapted + + +# --------------------------------------------------------------- evaluation -- +@torch.no_grad() +def eval_scheme(scheme, gen_fn, task, n_mc=200, batch=64, tau=0.45, model=None, + B=None, masks=None, encoder=None, rng_t=None, active_fn=None, + topk_mask=None): + """Returns (ser, cos) for one task/channel config.""" + s_acc = c_acc = 0.0 + for _ in range(n_mc): + E = gen_fn(batch) + if encoder is not None: + n, U, d = E.shape + E = encoder(E.reshape(-1, d)).reshape(n, U, d) + act = active_fn(E.shape[0]) if active_fn is not None else None + if scheme == "uwca": + ch = channel(E, **task) + Eh = model(ch["yI"], ch["yQ"], active=act, topk_mask=topk_mask) + elif scheme == "ofdma": + ch = channel(E, **task) + m = masks if masks is not None else block_masks(E.shape[1], E.shape[2], + E.device) + Eh = ofdma_decode(ch["yI"], m) + elif scheme == "sfdma": + ch = channel(E, **task) + m = masks + Eh = torch.stack([F.normalize((ch["yI"] * m[u]) / + (ch["h_hat"][:, u:u + 1] + 1e-8), dim=-1) + for u in range(m.shape[0])], 1) + elif scheme == "noma": + Eh = noma_sic_decode(E, task["snr_db"]) + elif scheme in ("lmmse_genie", "lmmse_blind"): + ch = channel(E, **task) + Eh = lmmse_decode(ch["yI"], ch["h_hat"], ch["nvar"], B, masks, + genie=(scheme == "lmmse_genie")) + elif scheme == "tdma_proj": + Eh = tdma_proj_decode(E, task["snr_db"], rng_t) + else: + raise ValueError(scheme) + s_acc += ser(Eh, E, tau, act) + c_acc += mean_cos(Eh, E, act) + return s_acc / n_mc, c_acc / n_mc + + +def save_json(name, obj): + p = DATA / name + with open(p, "w") as f: + json.dump(obj, f, indent=1) + print(f"saved -> {p}", flush=True) + + +def set_seed(seed=42): + torch.manual_seed(seed) + np.random.seed(seed) + return np.random.default_rng(seed) diff --git a/rev2/plot_rev2.py b/rev2/plot_rev2.py new file mode 100755 index 0000000..ebccc4b --- /dev/null +++ b/rev2/plot_rev2.py @@ -0,0 +1,153 @@ +"""Generate the five new revision figures from data/*.json into ../Relevance_TWCOM_R2/fig/. +Uniform geometry: 8:6 axes box, shared rcParams, no tight bounding box. +""" +import json +from pathlib import Path + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + +HERE = Path(__file__).resolve().parent +DATA = HERE / "data" +FIG = HERE.parent / "Relevance_TWCOM_R2" / "fig" +FIG.mkdir(exist_ok=True) + +plt.rcParams.update({ + "font.size": 9, "axes.labelsize": 10, "axes.titlesize": 10, + "legend.fontsize": 7.5, "xtick.labelsize": 8.5, "ytick.labelsize": 8.5, + "lines.linewidth": 1.4, "lines.markersize": 4.5, + "figure.dpi": 200, "savefig.dpi": 300, + "grid.alpha": 0.35, "axes.grid": True, +}) +AXRECT = [0.17, 0.165, 0.79, 0.80] # single panel 8:6-ish +FSIZE = (3.5, 2.75) + + +def newfig(): + f = plt.figure(figsize=FSIZE) + ax = f.add_axes(AXRECT) + return f, ax + + +def save(f, name, axes=None): + f.canvas.draw() + if axes: + for ax in axes: + for lbl in [ax.xaxis.label, ax.yaxis.label]: + bb = lbl.get_window_extent() + fw, fh = f.canvas.get_width_height() + assert bb.x0 >= -1 and bb.y0 >= -1 and bb.x1 <= fw + 1 \ + and bb.y1 <= fh + 1, f"label clipped in {name}" + f.savefig(FIG / name) + plt.close(f) + print("saved", FIG / name) + + +C = {"ofdma": "#546E7A", "blind": "#8D6E63", "noma": "#E65100", + "uwca": "#1565C0", "genie": "#2E7D32", "extra": "#C62828", + "aux": "#6A1B9A"} + +# ---------------------------------------------------------------- fig_fair -- +d = json.load(open(DATA / "e1_fair_baselines.json")) +snr = d["snr"] +f = plt.figure(figsize=(7.1, 2.75)) +axs = [f.add_axes([0.115, 0.165, 0.365, 0.77]), + f.add_axes([0.615, 0.165, 0.365, 0.77])] +for ax, sc, ttl in zip(axs, ["HIGH", "MIX"], ["(a) HIGH", "(b) MIX"]): + v = d["scenarios"][sc] + ax.semilogy(snr, v["ofdma"]["ser"], "s--", color=C["ofdma"], label="OFDMA") + ax.semilogy(snr, v["lmmse_blind"]["ser"], "v-", color=C["blind"], + markevery=(1, 2), label="LMMSE-blind") + ax.semilogy(snr, v["noma"]["ser"], "^-.", color=C["noma"], label="NOMA-SIC") + ax.semilogy(snr, v["uwca"]["ser"], "o-", color=C["uwca"], label="UWCA (prop.)") + ax.semilogy(snr, v["lmmse_genie"]["ser"], "d:", color=C["genie"], + label="LMMSE-genie") + ax.set_xlabel("SNR (dB)") + ax.set_ylabel("SER") + ax.set_title(ttl) + ax.set_xlim(0, 20) +axs[1].legend(loc="lower left", framealpha=0.9, fontsize=7) +save(f, "fig_fair.pdf", axs) + +# -------------------------------------------------------------- fig_resorth -- +d = json.load(open(DATA / "e6_residual_orth.json")) +snr = d["snr"] +f, ax = newfig() +ax.plot(snr, d["uwca"]["rho_input"], "k--", label=r"input $\rho(\mathbf{e}_u,\mathbf{e}_v)$") +ax.plot(snr, d["uwca"]["rho_decoded"], "o-", color=C["uwca"], + label=r"UWCA decoded $\rho(\hat{\mathbf{e}}_u,\hat{\mathbf{e}}_v)$") +ax.plot(snr, d["uwca"]["rho_residual"], "s-", color=C["extra"], + label=r"UWCA residual $\rho(\mathbf{r}_u,\mathbf{r}_v)$") +ax.plot(snr, d["ofdma"]["rho_decoded"], "^:", color=C["ofdma"], + label=r"OFDMA decoded") +ax.set_xlabel("SNR (dB)") +ax.set_ylabel("Pearson correlation") +ax.set_xlim(0, 20) +ax.set_ylim(-0.05, 0.62) +ax.legend(loc="upper right", framealpha=0.9, fontsize=6.8) +save(f, "fig_resorth.pdf", [ax]) + +# --------------------------------------------------------------- fig_phase2 -- +d = json.load(open(DATA / "e2_phase_iui.json")) +sg = d["sigma_phi_deg"] +f, ax = newfig() +sty = {"complexI_zerotrain": ("o-", C["uwca"], "mismatch-trained"), + "complexI_augtrain": ("s--", C["genie"], "phase-augmented"), + "complexIQ_iqtrain": ("^:", C["extra"], "two-rail (I/Q)")} +for key, (mk, col, lab) in sty.items(): + ax.plot(sg, d["curves"][key]["10.0"]["ser"], mk, color=col, label=lab) +for key, (mk, col, lab) in sty.items(): + ax.plot(sg, d["curves"][key]["20.0"]["ser"], mk, color=col, alpha=0.45, + label="_nolegend_") +ax.annotate("10 dB", xy=(1.5, 0.29), fontsize=8) +ax.annotate("20 dB", xy=(1.5, 0.135), fontsize=8) +ax.set_xlabel(r"phase residual $\sigma_\varphi$ (deg)") +ax.set_ylabel("SER") +ax.set_ylim(0.0, 0.45) +ax.legend(loc="upper left", framealpha=0.9, fontsize=7) +save(f, "fig_phase2.pdf", [ax]) + +# ---------------------------------------------------------------- fig_async -- +d = json.load(open(DATA / "e4_v3_async.json")) +dm = d["dmax"] +f, ax = newfig() +cur = d["curves"] +ax.plot(dm, [a[0] for a in cur["uwca_uncorrected"]["10.0"]], "o--", + color=C["uwca"], alpha=0.5, label="UWCA, uncorrected") +ax.plot(dm, [a[0] for a in cur["ofdma_uncorrected"]["10.0"]], "s--", + color=C["ofdma"], alpha=0.5, label="OFDMA, uncorrected") +ax.plot(dm, [a[0] for a in cur["uwca_corrected"]["10.0"]], "o-", + color=C["uwca"], label="UWCA, realigned") +ax.plot(dm, [a[0] for a in cur["ofdma_corrected"]["10.0"]], "s-", + color=C["ofdma"], label="OFDMA, realigned") +ax.plot(dm, [a[0] for a in cur["uwca_corrected_err20"]["10.0"]], "^-.", + color=C["extra"], label="UWCA, realigned (20% est. err.)") +ax.set_xlabel(r"maximum timing offset $\Delta$ (symbols)") +ax.set_ylabel("SER") +ax.set_ylim(0, 1.05) +ax.legend(loc="lower right", ncol=2, framealpha=0.9, fontsize=6.0) +save(f, "fig_async.pdf", [ax]) + +# ------------------------------------------------------------- fig_dynusers -- +d = json.load(open(DATA / "e3_dynamic_users.json")) +ks = d["k"] +f = plt.figure(figsize=FSIZE) +ax = f.add_axes([0.20, 0.165, 0.76, 0.80]) +ax.plot(ks, [a[1] for a in d["fixed8"]["10.0"]], "o-", color=C["uwca"], + label="single model, 10 dB") +ax.plot(ks, [a[1] for a in d["fixed8"]["20.0"]], "o--", color=C["uwca"], + alpha=0.5, label="single model, 20 dB") +ok = sorted(int(k) for k in d["oracle"]) +ax.plot(ok, [d["oracle"][str(k)]["10.0"][1] for k in ok], "s", ls="none", + color=C["extra"], label="per-count retrained, 10 dB") +ax.plot(ok, [d["oracle"][str(k)]["20.0"][1] for k in ok], "s", ls="none", + mfc="none", color=C["extra"], label="per-count retrained, 20 dB") +ax.set_xlabel(r"number of active users $|\mathcal{A}|$") +ax.set_ylabel(r"mean cosine $\bar{c}$") +ax.set_ylim(0.28, 0.47) +ax.legend(loc="upper left", framealpha=0.9, fontsize=6.8) +save(f, "fig_dynusers.pdf", [ax]) + +print("ALL FIGURES DONE")