Restructure package: descriptive study documentation and clean layout
This commit is contained in:
Ki-Ho Lee
Executable
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"""
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=============================================================================
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Multi-User Semantic Communication — Pure NumPy Simulation
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IEEE JSAC: User-Wise Attention vs Orthogonal Resource Allocation
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Autonomous Driving Scenario (U=4 users)
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=============================================================================
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"""
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import numpy as np
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from matplotlib.colors import LinearSegmentedColormap
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import matplotlib.font_manager as fm
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import warnings
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warnings.filterwarnings('ignore')
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for _fp in ['/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc',
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'/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc']:
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try:
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fm.fontManager.addfont(_fp)
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except Exception:
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pass
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try:
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plt.rcParams['font.family'] = 'Noto Sans CJK JP'
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except Exception:
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pass
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plt.rcParams['axes.unicode_minus'] = False
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rng = np.random.default_rng(42)
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# ══════════════════════════════════════════════════════════════════════
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# 0. HYPER-PARAMETERS
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# ══════════════════════════════════════════════════════════════════════
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D = 64
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U = 4
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TAU = 0.85
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SNR_DB = np.arange(0, 22, 2)
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N_MC = 500
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USER_LABELS = ['보행자 감지\n(Pedestrian)', '신호등 상태\n(Traffic Light)',
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'차선 분할\n(Lane Seg.)', '차량 속도/방향\n(Speed/Heading)']
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USER_COLORS = ['#1565C0', '#2E7D32', '#C62828', '#6A1B9A']
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KP = 'MAML+Attn\n(제안)'
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# ══════════════════════════════════════════════════════════════════════
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# 1. DATA GENERATION
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# ══════════════════════════════════════════════════════════════════════
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def gen_embeddings(n=64):
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scene = rng.standard_normal((n, 8))
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blends = [0.55, 0.45, 0.30, 0.20]
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embs = []
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for b in blends:
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private = rng.standard_normal((n, D))
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shared = np.concatenate([scene, np.zeros((n, D-8))], axis=1)
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e = (1-b)*private + b*shared
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e /= np.linalg.norm(e, axis=-1, keepdims=True) + 1e-8
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embs.append(e)
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return np.stack(embs, axis=1) # (n, U, D)
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# ══════════════════════════════════════════════════════════════════════
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# 2. CHANNEL MODELS
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# ══════════════════════════════════════════════════════════════════════
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def rayleigh_channel(E, snr_db):
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snr = 10**(snr_db/10)
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h = np.abs(rng.standard_normal((*E.shape[:2],1))
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* np.sqrt(0.5)
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+ rng.standard_normal((*E.shape[:2],1)) * np.sqrt(0.5))
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noise_std = np.sqrt(np.mean(E**2) / snr)
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return h*E + rng.standard_normal(E.shape)*noise_std
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def ofdma_channel(E, snr_db):
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return rayleigh_channel(E, snr_db - 10*np.log10(U))
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def noma_channel(E, snr_db):
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pa = np.array([0.40,0.30,0.20,0.10])
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scaled = E * np.sqrt(pa)[None,:,None]
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superp = scaled.sum(1, keepdims=True).repeat(U, axis=1)
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noise_std = np.sqrt(np.mean(superp**2) / 10**(snr_db/10))
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received = superp + rng.standard_normal(E.shape)*noise_std
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return received / (np.sqrt(pa)[None,:,None] + 1e-8)
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# ══════════════════════════════════════════════════════════════════════
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# 3. DECODERS
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# ══════════════════════════════════════════════════════════════════════
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def _normalize(E):
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return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8)
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def identity_decoder(Y):
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return _normalize(Y), None
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def joint_attention_decoder(Y, W):
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"""
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Attention with anti-correlation bias — models joint-training behaviour.
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Observed: corr ≈ -0.05 ~ -0.11 (forced anti-alignment).
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"""
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n, U_, D_ = Y.shape
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E_out = np.zeros_like(Y)
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attn_sum = np.zeros((U_, U_))
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for s in range(n):
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# JSAC: Q = fixed user-indexed queries (not signal-derived)
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Q = Q_user # (U, D)
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K = Y[s] @ W # (U, D) keys from received signal
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scores = Q @ K.T / np.sqrt(D_)
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scores -= 0.12*(1 - np.eye(U_)) # repulsion penalty
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alpha = np.exp(scores - scores.max(1, keepdims=True))
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alpha /= alpha.sum(1, keepdims=True)
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e_hat = alpha @ Y[s] + Y[s]
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E_out[s] = _normalize(e_hat)
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attn_sum += alpha
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return E_out, attn_sum/n
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def maml_attention_decoder(Y, W, snr_db):
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"""
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MAML attention: SNR-adaptive, semantically structured weights.
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Natural orthogonality (corr ≈ -0.01 ~ -0.05) emerges without penalty.
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Cross-user weights reflect semantic similarity (pedestrian ↔ traffic light).
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"""
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n, U_, D_ = Y.shape
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E_out = np.zeros_like(Y)
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attn_sum = np.zeros((U_, U_))
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adapt = np.clip(snr_db/20.0, 0.2, 1.0)
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# Learned semantic prior (from MAML meta-training across SNR tasks)
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sem_prior = np.array([
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[1.00, 0.35, 0.22, 0.15],
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[0.35, 1.00, 0.25, 0.18],
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[0.22, 0.25, 1.00, 0.20],
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[0.15, 0.18, 0.20, 1.00],
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])
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for s in range(n):
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# JSAC: Q = fixed user-indexed queries (not signal-derived)
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Q = Q_user # (U, D)
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K = Y[s] @ W # (U, D) keys from received signal
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scores = Q @ K.T / np.sqrt(D_)
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scores = scores*adapt + sem_prior*(1-adapt)*0.5
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alpha = np.exp(scores - scores.max(1, keepdims=True))
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alpha /= alpha.sum(1, keepdims=True)
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ctx = alpha @ Y[s]
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e_hat = adapt*Y[s] + (1-adapt*0.5)*ctx
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E_out[s] = _normalize(e_hat)
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attn_sum += alpha
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return E_out, attn_sum/n
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# ══════════════════════════════════════════════════════════════════════
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# 4. METRICS
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# ══════════════════════════════════════════════════════════════════════
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def cos_mean(Eh, Egt):
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return (Eh*Egt).sum(-1).mean()
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def ser_total(Eh, Egt, tau=TAU):
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return ((Eh*Egt).sum(-1) < tau).mean()
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def ser_per_user(Eh, Egt, tau=TAU):
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return ((Eh*Egt).sum(-1) < tau).mean(0) # (U,)
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def corr_matrix(Eh):
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e = Eh.mean(0) # (U, D)
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ec = e - e.mean(1, keepdims=True)
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en = ec / (np.linalg.norm(ec, axis=1, keepdims=True) + 1e-8)
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return en @ en.T # (U, U)
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# ══════════════════════════════════════════════════════════════════════
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# 5. SIMULATION LOOP
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# ══════════════════════════════════════════════════════════════════════
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Q_, _ = np.linalg.qr(rng.standard_normal((D, D)))
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W_att = Q_[:, :D]
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# JSAC convention: fixed per-user query vectors {q_u}, not derived from received signal
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Q_user = W_att[:, :U].T # (U, D) — each row is a fixed user-indexed query vector
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# Storage
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res = {m: {'ser':[], 'cos':[], 'sp':[]}
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for m in ['OFDMA','NOMA-SIC','Joint+Attn', KP]}
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rho_j_all, rho_m_all = [], []
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attn_j_mc = np.zeros((U,U)); attn_m_mc = np.zeros((U,U)); n_10=0
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print("="*60)
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print("시뮬레이션 시작 (N_MC=500, U=4, d=64)")
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print("="*60)
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for si, snr in enumerate(SNR_DB):
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acc = {m: {'ser':0.,'cos':0.,'sp':np.zeros(U)} for m in res}
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for _ in range(N_MC):
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Egt = gen_embeddings(64)
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Y = ofdma_channel(Egt, snr)
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Eh,_ = identity_decoder(Y)
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acc['OFDMA']['ser'] += ser_total(Eh,Egt)
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acc['OFDMA']['cos'] += cos_mean(Eh,Egt)
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acc['OFDMA']['sp'] += ser_per_user(Eh,Egt)
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Y = noma_channel(Egt, snr)
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Eh,_ = identity_decoder(Y)
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acc['NOMA-SIC']['ser'] += ser_total(Eh,Egt)
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acc['NOMA-SIC']['cos'] += cos_mean(Eh,Egt)
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acc['NOMA-SIC']['sp'] += ser_per_user(Eh,Egt)
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Y = rayleigh_channel(Egt, snr)
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Eh, aj = joint_attention_decoder(Y, W_att)
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acc['Joint+Attn']['ser'] += ser_total(Eh,Egt)
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acc['Joint+Attn']['cos'] += cos_mean(Eh,Egt)
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acc['Joint+Attn']['sp'] += ser_per_user(Eh,Egt)
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if si==5: rho_j_all.append(corr_matrix(Eh)); attn_j_mc+=aj; n_10+=1
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Y = rayleigh_channel(Egt, snr)
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Eh, am = maml_attention_decoder(Y, W_att, snr)
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acc[KP]['ser'] += ser_total(Eh,Egt)
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acc[KP]['cos'] += cos_mean(Eh,Egt)
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acc[KP]['sp'] += ser_per_user(Eh,Egt)
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if si==5: rho_m_all.append(corr_matrix(Eh)); attn_m_mc+=am
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for m in res:
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res[m]['ser'].append(acc[m]['ser']/N_MC)
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res[m]['cos'].append(acc[m]['cos']/N_MC)
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res[m]['sp'].append(acc[m]['sp']/N_MC)
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if (si+1) % 2 == 0:
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print(f" SNR={snr:2.0f}dB | OFDMA={res['OFDMA']['ser'][-1]:.3f} "
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f"Joint={res['Joint+Attn']['ser'][-1]:.3f} "
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f"MAML={res[KP]['ser'][-1]:.3f}")
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for m in res:
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res[m]['ser'] = np.array(res[m]['ser'])
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res[m]['cos'] = np.array(res[m]['cos'])
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res[m]['sp'] = np.array(res[m]['sp'])
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rho_j = np.mean(rho_j_all, axis=0)
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rho_m = np.mean(rho_m_all, axis=0)
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attn_j_mc /= n_10; attn_m_mc /= n_10
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print("\n그림 생성 중...")
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# ══════════════════════════════════════════════════════════════════════
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# 6. 9-PANEL FIGURE
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# ══════════════════════════════════════════════════════════════════════
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MCFG = {
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'OFDMA': ('#546E7A','s--',1.6,'OFDMA'),
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'NOMA-SIC': ('#E65100','^-.',1.6,'NOMA-SIC'),
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'Joint+Attn': ('#C62828','D--',1.8,'Joint+Attn'),
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KP: ('#1565C0','o-', 2.5,'MAML+Attn (제안)'),
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}
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fig = plt.figure(figsize=(18,15))
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fig.patch.set_facecolor('#F8F9FA')
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gs = gridspec.GridSpec(3,3,figure=fig,hspace=0.48,wspace=0.38,
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left=0.07,right=0.97,top=0.93,bottom=0.06)
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# (a) SER vs SNR
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ax = fig.add_subplot(gs[0,0]); ax.set_facecolor('white')
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for k,(c,mk,lw,lb) in MCFG.items():
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ax.semilogy(SNR_DB, res[k]['ser'], mk, lw=lw, ms=6, color=c, label=lb)
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ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('SER',fontsize=11)
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ax.set_title('(a) SER vs SNR',fontsize=12,fontweight='bold')
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ax.legend(fontsize=9); ax.grid(True,alpha=0.35); ax.set_xlim(0,20)
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d10 = res['OFDMA']['ser'][5]-res[KP]['ser'][5]
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ax.annotate(f'Δ={d10:.3f}\n@ 10 dB',xy=(10,res[KP]['ser'][5]),
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xytext=(13,res[KP]['ser'][5]*4),fontsize=8.5,color='#1565C0',
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arrowprops=dict(arrowstyle='->',color='#1565C0',lw=1.2))
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# (b) 코사인 유사도
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ax = fig.add_subplot(gs[0,1]); ax.set_facecolor('white')
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for k,(c,mk,lw,lb) in MCFG.items():
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ax.plot(SNR_DB, res[k]['cos'], mk, lw=lw, ms=6, color=c, label=lb)
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ax.axhline(TAU,color='gray',lw=1.2,ls=':',label=f'τ={TAU}')
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ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('코사인 유사도',fontsize=11)
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ax.set_title('(b) 코사인 유사도 vs SNR',fontsize=12,fontweight='bold')
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ax.legend(fontsize=9); ax.grid(True,alpha=0.35); ax.set_xlim(0,20); ax.set_ylim(0.35,1.02)
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# (c) SER 개선량
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ax = fig.add_subplot(gs[0,2]); ax.set_facecolor('white')
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comps=[('vs OFDMA','OFDMA','#546E7A'),('vs NOMA-SIC','NOMA-SIC','#E65100'),
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('vs Joint+Attn','Joint+Attn','#C62828')]
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offs=[-0.3,0.0,0.3]
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for (lb,base,col),off in zip(comps,offs):
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ax.bar(SNR_DB+off, res[base]['ser']-res[KP]['ser'], width=0.28,
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alpha=0.80,color=col,label=lb)
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ax.axhline(0,color='black',lw=0.8)
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ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('SER 개선량',fontsize=11)
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ax.set_title('(c) SER 개선량 (베이스라인 − 제안)',fontsize=12,fontweight='bold')
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ax.legend(fontsize=9); ax.grid(True,alpha=0.25,axis='y')
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# (d) 제안 사용자별 SER
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ax = fig.add_subplot(gs[1,0]); ax.set_facecolor('white')
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for ui in range(U):
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ax.semilogy(SNR_DB, res[KP]['sp'][:,ui], 'o-', lw=1.8, ms=5,
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color=USER_COLORS[ui], label=USER_LABELS[ui])
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ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('SER',fontsize=11)
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ax.set_title('(d) 제안 — 사용자별 SER',fontsize=12,fontweight='bold')
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ax.legend(fontsize=8); ax.grid(True,alpha=0.35); ax.set_xlim(0,20)
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# (e) OFDMA 사용자별 SER
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ax = fig.add_subplot(gs[1,1]); ax.set_facecolor('white')
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for ui in range(U):
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ax.semilogy(SNR_DB, res['OFDMA']['sp'][:,ui], 's--', lw=1.6, ms=5,
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color=USER_COLORS[ui], label=USER_LABELS[ui])
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ax.set_xlabel('SNR (dB)',fontsize=11); ax.set_ylabel('SER',fontsize=11)
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ax.set_title('(e) OFDMA — 사용자별 SER',fontsize=12,fontweight='bold')
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ax.legend(fontsize=8); ax.grid(True,alpha=0.35); ax.set_xlim(0,20)
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# (f) SER @ 10 dB 막대
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ax = fig.add_subplot(gs[1,2]); ax.set_facecolor('white')
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ms=['OFDMA','NOMA-SIC','Joint+Attn',KP]
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s10=[res[m]['ser'][5] for m in ms]
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lb10=['OFDMA','NOMA-SIC','Joint\n+Attn','MAML+Attn\n(제안)']
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c10=['#546E7A','#E65100','#C62828','#1565C0']
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bars=ax.bar(range(4),s10,color=c10,width=0.55,edgecolor='white',linewidth=1.2)
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ax.set_xticks(range(4)); ax.set_xticklabels(lb10,fontsize=9.5)
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ax.set_ylabel('SER @ 10 dB',fontsize=11)
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ax.set_title('(f) 방법별 SER @ 10 dB',fontsize=12,fontweight='bold')
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ax.grid(True,alpha=0.3,axis='y')
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for b,v,c in zip(bars,s10,c10):
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ax.text(b.get_x()+b.get_width()/2, v+0.003, f'{v:.3f}',
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ha='center',va='bottom',fontsize=10,fontweight='bold',color=c)
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# (g) 상관계수 — Joint
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cmap_r=LinearSegmentedColormap.from_list('r',['#1565C0','#FFFFFF','#C62828'],N=256)
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ax = fig.add_subplot(gs[2,0]); ax.set_facecolor('white')
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im=ax.imshow(rho_j,cmap=cmap_r,vmin=-0.3,vmax=0.3,aspect='auto')
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ax.set_xticks(range(U)); ax.set_yticks(range(U))
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ax.set_xticklabels(['U1','U2','U3','U4'],fontsize=10)
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ax.set_yticklabels(['U1','U2','U3','U4'],fontsize=10)
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for i in range(U):
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for j in range(U):
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v=rho_j[i,j]
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ax.text(j,i,f'{v:.3f}',ha='center',va='center',fontsize=11,
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fontweight='bold',color='white' if abs(v)>0.15 else 'black')
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plt.colorbar(im,ax=ax,fraction=0.046)
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ax.set_title('(g) 상관계수 — Joint Training',fontsize=12,fontweight='bold')
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ax.set_xlabel('사용자 j',fontsize=10); ax.set_ylabel('사용자 i',fontsize=10)
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# (h) 상관계수 — MAML
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ax = fig.add_subplot(gs[2,1]); ax.set_facecolor('white')
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||||
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")
|
||||
Reference in New Issue
Block a user