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Author SHA1 Message Date
KiHoLee 4e88b33ecd Add the raw result files behind Fig. 2 and Fig. 5 
The README table pointed at simulation/results/ for the SER-vs-SNR and
real-data figures, but only realdata_analytic.json was present. This adds
the five trained_*.json scenario records, realdata_train.json, and the 13
CSV files the legacy figures read.
 2026-08-26 14:44:52 +09:00
KiHoLee 81c2be1d78 Verify the appendix bound against its stated constant 
The check tested f(gamma) >= gamma/(gamma+1), but the manuscript claims
f(gamma) >= c1*gamma/(gamma+1) with c1 >= 0.97. It now tests the stated
constant and records the achieved one, 0.9711.
 2026-08-26 13:53:55 +09:00
KiHoLee 48f12dea74 Panel tags quote the exact full-dataset cosines (0.427/-0.050/0.064) 2026-08-26 09:20:43 +09:00
KiHoLee 97bbfb1617 Archive analytic real-data curves (incl. SFDMA) as stored artifact 2026-08-25 23:02:19 +09:00
KiHoLee 344ae1b97d Round-2 audit: UWCA (genie) label, legend dictionary ported into legacy plot scripts 2026-08-25 22:32:15 +09:00
KiHoLee ab1770c6d0 Fig. 1 grayscale styling 2026-08-25 22:19:43 +09:00
KiHoLee 5020ab0569 Legend dictionary: UWCA (trained) in fairness figure, UWCA (oracle) in real-data figure 2026-08-25 22:11:43 +09:00
KiHoLee 587a653f6b Fig. 1: narrower decoder panel 2026-08-25 21:49:48 +09:00
KiHoLee 689e29496d Fig. 1: gray decoder panel, enlarged sum node, connection fixes 2026-08-25 21:43:50 +09:00
KiHoLee f55524cb05 Update figures and docs: OMA naming, uniform legends, TikZ Fig. 1 source 
- Rename the orthogonal comparison scheme OFDMA -> OMA in docs and figure legends (data keys keep ofdma)
- Legend labels normalized to the paper dictionary (OMA, SFDMA, UWCA (proposed))
- New TikZ-generated system overview figure with text-consistent notation (soft decoder masks m-tilde, queries q_u, sharpness eta)
- Regenerated fig_fair, fig_resorth, fig_realdata_c, fig_async, fig_dynusers; fig_ser_all legend patched
 2026-08-25 21:39:53 +09:00
36 changed files with 4885 additions and 2415 deletions
Expand all files Collapse all files
README.md
+1 -1
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@@ -70,7 +70,7 @@ Each study script is self-contained and writes its JSON into
inequality in the manuscript with standalone numpy code (no experiment code
reused): the relevance identity, the mutual-information correlation, the
subspace ceiling, the LMMSE receiver against an empirical Wiener solution
(0.1% MSE agreement; the blind form matches the OFDMA cosine to machine
(0.1% MSE agreement; the blind form matches the OMA cosine to machine
precision), and the surrogate bound used in the appendix (uniform constant
0.97). Results: `experiments/verification/math_verify.json`.
docs/EXPERIMENTS.md
+8 -8
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@@ -36,7 +36,7 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
channel gains, the manuscript's Proposition on optimal linear receivers),
and `tdma_proj` (random orthonormal 16-dim projection per user — an
arbitrary orthogonal partition).
- **Key results.** lmmse_blind = OFDMA at every SNR in all scenarios
- **Key results.** lmmse_blind = OMA at every SNR in all scenarios
(SER 0.509 vs 0.508 @10 dB HIGH), and tdma_proj and SFDMA coincide with
them — the ceiling binds every correlation-blind receiver. HIGH @20 dB:
blind 0.327 -> UWCA 0.110 -> genie 0.044, so UWCA recovers 77% of the
@@ -95,16 +95,16 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
- **Setup.** Per-user integer offsets delta_u ~ U{0..Delta} symbols,
Delta in {0,1,2,4,8}, shift each user's transmitted block within the
frame (edge energy lost). Conditions: uncorrected reception (UWCA and
OFDMA), block-wise realignment using pilot-estimated offsets (each
OMA), block-wise realignment using pilot-estimated offsets (each
user's block region shifted back individually), and realignment with a
deliberately impaired estimator (+-1 symbol on 20% of users).
- **Key results.** Uncorrected offsets are catastrophic for *every*
embedding-level scheme (one symbol: UWCA 0.26 -> 0.73, OFDMA 0.51 ->
embedding-level scheme (one symbol: UWCA 0.26 -> 0.73, OMA 0.51 ->
0.76), because i.i.d. embedding coordinates fully decorrelate under a
one-symbol misalignment — synchronization is a shared physical-layer
prerequisite, not a property of the multiple-access mechanism. With
realignment the degradation is gradual (0.266 -> 0.304 @Delta=1, 0.455
@Delta=8) and realigned UWCA stays below realigned OFDMA (0.536-0.638)
@Delta=8) and realigned UWCA stays below realigned OMA (0.536-0.638)
at every offset. The impaired estimator costs 0.14 SER: whole-symbol
residuals sacrifice the affected block, so timing must be held to
sub-symbol accuracy (which the closed-loop timing advance provides).
@@ -119,10 +119,10 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
two-layer tanh view networks g_u per user (seed 7): users share the
scene s only through independent nonlinear transformations. Cases:
shared scene vs independent scenes (control). Schemes: trained UWCA,
OFDMA, NOMA-SIC, and the scalar-parameterized genie LMMSE fed the
OMA, NOMA-SIC, and the scalar-parameterized genie LMMSE fed the
*measured* mean pairwise cosine.
- **Key results.** The linear correlation is destroyed (mean cosine
0.006), so the genie LMMSE collapses onto the ceiling (0.515 vs OFDMA
0.006), so the genie LMMSE collapses onto the ceiling (0.515 vs OMA
0.518 @10 dB) — no scalar or linear receiver can represent the shared
structure. UWCA still attains 0.363 @10 dB / 0.220 @20 dB. The
independent-scene control (UWCA 0.441) isolates the manifold-prior
@@ -138,11 +138,11 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
- **Setup.** E1's trained HIGH decoder; per-sample Pearson correlation
across the 64 dimensions, averaged over user pairs and 100x64 samples
per SNR, for: input embeddings, decoded embeddings, and decoding
residuals r_u = e_hat_u - e_u; OFDMA decoded correlation as reference.
residuals r_u = e_hat_u - e_u; OMA decoded correlation as reference.
- **Key results.** The decoded-embedding correlation rises with SNR from
0.31 toward the 0.39 input level (the shared content is delivered, not
stripped), while the residual correlation falls 0.23 -> 0.14 (2.8x below
the input level) — the emergent residual orthogonality. OFDMA's decoded
the input level) — the emergent residual orthogonality. OMA's decoded
correlation is 0.00 at every SNR: orthogonal access erases the
inter-user semantic structure from the delivered embeddings.
- **Artifacts.** `experiments/e6_residual_orth.py` ->
experiments/make_figures.py
+5 -5
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@@ -61,11 +61,11 @@ axs = [f.add_axes([0.115, AXB, 0.365, AXH]),
f.add_axes([0.615, AXB, 0.365, AXH])]
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["ofdma"]["ser"], "s--", color=C["ofdma"], label="OMA")
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["uwca"]["ser"], "o-", color=C["uwca"], label="UWCA (trained)")
ax.semilogy(snr, v["lmmse_genie"]["ser"], "d:", color=C["genie"],
label="LMMSE-genie")
ax.set_xlabel("SNR (dB)")
@@ -86,7 +86,7 @@ ax.plot(snr, d["uwca"]["rho_decoded"], "o-", color=C["uwca"],
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")
label=r"OMA decoded")
ax.set_xlabel("SNR (dB)")
ax.set_ylabel("Pearson correlation")
ax.set_xlim(0, 20)
@@ -122,11 +122,11 @@ 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")
color=C["ofdma"], alpha=0.5, label="OMA, 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")
color=C["ofdma"], label="OMA, 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)")
experiments/verification/math_verify.json
+3 -2
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@@ -28,8 +28,9 @@
"max_abs_cos_diff": 4.440892098500626e-16
},
"appendix_f_bound": {
"holds_on_grid": false,
"worst_violation": 0.027053603772970947
"claimed_c1": 0.97,
"holds_on_grid": true,
"achieved_c1": 0.9710759305294567
},
"appendix_exp_ineq": {
"holds": true
experiments/verification/math_verify.py
+8 -7
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@@ -109,14 +109,15 @@ out["prop3_blind_eq_ofdma"] = {"max_abs_cos_diff": float(np.abs(cb - co).max())}
# h ~ Rayleigh(1/sqrt2), sigma^2 = 1/gamma
gams = 10 ** (np.arange(-10, 21, 2) / 10)
h = rng.rayleigh(scale=np.sqrt(0.5), size=1_000_000)
ok, worst = True, 0.0
C1 = 0.97
ok, ratio = True, np.inf
for g in gams:
f = float((h / np.sqrt(h ** 2 + 1 / g)).mean())
bound = g / (g + 1)
ok &= f >= bound
worst = max(worst, bound - f)
out["appendix_f_bound"] = {"holds_on_grid": bool(ok),
"worst_violation": worst}
ok &= f >= C1 * g / (g + 1)
ratio = min(ratio, f / (g / (g + 1)))
out["appendix_f_bound"] = {"claimed_c1": C1,
"holds_on_grid": bool(ok),
"achieved_c1": float(ratio)}
# (6) beta*exp(x) >= beta*x => beta e^{eta' beta gamma} >= eta' beta^2 gamma
xs = rng.uniform(0, 20, 10000)
@@ -129,5 +130,5 @@ out["concentration_residual"] = {"d64_residual": 1 / np.sqrt(64),
"claimed_max": 0.13}
print(json.dumps(out, indent=1))
with open("math_verify.json", "w") as f:
with open("data/math_verify.json", "w") as f:
json.dump(out, f, indent=1)
fig/fig0_overview2.pdf
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fig/fig0_overview2_tikz.tex
Executable
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@@ -0,0 +1,91 @@
\documentclass[tikz,border=4pt]{standalone}
\usepackage{amsmath,amssymb,bm}
\usetikzlibrary{positioning,shapes.geometric,decorations.pathreplacing,calc,fit,backgrounds}
\begin{document}
\begin{tikzpicture}[
font=\small,
box/.style={draw,rounded corners=1pt,minimum width=7.5mm,minimum height=5.5mm,inner sep=1.5pt},
enc/.style={box},
chan/.style={draw,diamond,aspect=1.4,fill=gray!25,inner sep=0.8pt},
rxb/.style={box},
mult/.style={draw,circle,inner sep=0.4pt,minimum size=4mm},
sum/.style={draw,rounded corners=2.5mm,fill=gray!15,minimum width=12mm,minimum height=36mm},
dec/.style={draw,rounded corners=2pt,minimum width=53mm,minimum height=6mm,inner sep=2pt},
lbl/.style={font=\small\bfseries,align=center},
arr/.style={-stealth,semithick}]
% ---------------- left panel: transmitters / channel ----------------
\foreach \i/\yy in {1/1.7, 2/0.45}{
\node[box] (x\i) at (0,\yy) {$\mathbf{x}_{\i}$};
\node[enc,right=4.5mm of x\i] (f\i) {$f_\phi$};
\node[mult,right=5.5mm of f\i] (o\i) {$\odot$};
\node[chan,right=5mm of o\i] (h\i) {$\tilde h_{\i}$};
\node[rxb,right=5mm of h\i] (y\i) {$\mathbf{y}_{\i}$};
\draw[arr] (x\i) -- (f\i);
\draw[arr] (f\i) -- node[above,font=\footnotesize]{$\mathbf{e}_{\i}$} (o\i);
\draw[arr] (o\i) -- (h\i);
\draw[arr] (h\i) -- (y\i);
\node[below=1.8mm of o\i,font=\footnotesize] (m\i) {$\mathbf{m}_{\i}$};
\draw[arr] (m\i) -- (o\i);
}
\node[box] (xU) at (0,-1.2) {$\mathbf{x}_{U}$};
\node[enc,right=4.5mm of xU] (fU) {$f_\phi$};
\node[mult,right=5.5mm of fU] (oU) {$\odot$};
\node[chan,right=5mm of oU] (hU) {$\tilde h_{U}$};
\node[rxb,right=5mm of hU] (yU) {$\mathbf{y}_{U}$};
\draw[arr] (xU) -- (fU);
\draw[arr] (fU) -- node[above,font=\footnotesize]{$\mathbf{e}_{U}$} (oU);
\draw[arr] (oU) -- (hU);
\draw[arr] (hU) -- (yU);
\node[below=1.8mm of oU,font=\footnotesize] (mU) {$\mathbf{m}_{U}$};
\draw[arr] (mU) -- (oU);
\foreach \c in {x,f,o,h,y}{\node at ($(\c 2)!0.5!(\c U)$) {$\vdots$};}
% superposition
\node[sum,right=9mm of y2.east,yshift=-2mm] (sig)
{$\displaystyle\sum_{v=1}^{U}$};
\draw[arr] (y1.east) -- (y1.east -| sig.west);
\draw[arr] (y2.east) -- (y2.east -| sig.west);
\draw[arr] (yU.east) -- (yU.east -| sig.west);
% bottom labels
\node[lbl,below=5.5mm of fU] {Transmitters\\(shared $f_\phi$)};
\node[lbl,below=5.5mm of hU] {Rayleigh\\Channel};
\node[lbl] at ([yshift=-4.5mm]sig.south) {Received Signal\\(Superposition $\tilde{\mathbf{y}}$)};
% ---------------- right panel: UWCA decoder ----------------
\node[dec,fill=gray!15,right=26mm of sig.east,yshift=19mm] (mask)
{Soft Masks $\{\tilde{\mathbf{m}}_i\}_{i=1}^{U}$};
\node[dec,fill=gray!15,below=2.2mm of mask,align=center] (proj)
{K/V Projection $(\mathbf{W}_K,\mathbf{W}_V)$\\[-1pt] Queries $\{\mathbf{q}_u\}$};
\node[dec,fill=gray!30,below=2.2mm of proj,minimum height=9mm,align=center] (attn)
{Scaled Dot-Product Attention\\[-1pt]
{\footnotesize $\alpha_{u,i}=\mathrm{softmax}\big(\eta\,\mathbf{q}_u^{\top}\mathbf{k}_i/\sqrt{d_k}\big)$}};
\node[mult,below=2.2mm of attn] (plus) {$\oplus$};
\node[dec,fill=gray!15,below=2.2mm of plus] (norm) {$\ell_2$-Normalize};
\node[dec,fill=gray!15,below=2.2mm of norm] (out)
{$\hat{\mathbf{e}}_1,\ \hat{\mathbf{e}}_2,\ \ldots,\ \hat{\mathbf{e}}_U$};
\draw[arr] (mask) -- (proj);
\draw[arr] (proj) -- (attn);
\draw[arr] (attn) -- (plus);
\draw[arr] (plus) -- (norm);
\draw[arr] (norm) -- (out);
\draw[arr] (sig.east) -- node[above,pos=0.45,font=\small]{$\tilde{\mathbf{y}}$} ++(13mm,0) |- (mask.west);
\draw[arr,dashed] (mask.east) -- ++(4mm,0) |- (plus.east)
node[pos=0.78,above,font=\footnotesize]{skip: $\tilde{\mathbf{y}}\odot\tilde{\mathbf{m}}_u$};
% decoder panel frame
\begin{scope}[on background layer]
\node[rounded corners=2mm,fill=gray!7,fit=(mask)(out)(attn),
inner xsep=7mm,inner ysep=4.5mm] (panel) {};
\end{scope}
\node[anchor=south,font=\small\bfseries\color{black!75},inner sep=2pt] at (panel.north)
{UWCA Decoder (per-user, shared weights $\theta$)};
% MAML brace
\draw[decorate,decoration={brace,mirror,amplitude=2mm},black!75,thick]
([yshift=-1.5mm]panel.south west) -- ([yshift=-1.5mm]panel.south east)
node[midway,below=2mm,font=\small\color{black!75}]
{MAML meta-training over SNR tasks $\{\mathcal{T}_k\}_{k=1}^{K}$};
\end{tikzpicture}
\end{document}
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simulation/plot_figures.py
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simulation/realdata_plot.py
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@@ -1,128 +1,134 @@
"""Real-data (digits) Fig. 5 generator: downstream classification accuracy vs SNR
for HIGH/LOW/MIX, parallel to the synthetic Fig. 2.
Curves per panel:
- OFDMA [division], SFDMA [feature div.], NOMA-SIC (analytical baselines)
- UWCA (analytical) : oracle-beta cross-attention (relevance SUPPLIED) -- dotted
- UWCA w/o MAML : decoder TRAINED on real digits, no meta-learning (from realdata_train.json)
- UWCA w/ MAML : decoder TRAINED on real digits with MAML (proposed) -- hollow circles
Trained curves are read from results/realdata_train.json (produced by
revision_realdata_train.py); the analytical / baseline curves are recomputed here
so they share one Monte-Carlo setting. Saves results/fig_realdata_c.pdf.
"""
import json, numpy as np
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
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)
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)
def _norm(E): return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8)
def se_channel(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)
nstd = np.sqrt(float(np.mean(Ytx**2))/(10**(snr/10)))
return h*Ytx[:, None, :] + RNG.standard_normal((n, U, D))*nstd
def ofdma(Y): return np.stack([_norm(Y[:, u, :]*MASKS[u]) for u in range(U)], 1)
def sfdma(E, snr):
"""Full-band, orthogonal semantic-subspace division (block basis)."""
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)
X_ = E * MASKS[None]
yv = (h * X_).sum(1)
yv = yv + RNG.standard_normal((n, D))*np.sqrt(float(np.mean(yv**2))/(10**(snr/10)))
return np.stack([_norm((yv * MASKS[u])/(h[:, u, :]+1e-8)) for u in range(U)], 1)
def noma_ch(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)))
return yv, h
def noma_sic(yv, h):
n = yv.shape[0]; Eh = np.zeros((n, U, D)); res = yv.copy()
for u in range(U):
Eh[:, u, :] = _norm(res/(h[:, u, :]+1e-8)); res -= h[:, u, :]*np.sqrt(NOMA_POWER[u])*Eh[:, u, :]
return Eh
def uwca_oracle(Y, bmc):
"""Analytical UWCA: oracle-beta cross-attention (relevance supplied)."""
R = Y[:, :, None, :]*MASKS[None, None]
a = bmc.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(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}")
"""Real-data (digits) Fig. 5 generator: downstream classification accuracy vs SNR
for HIGH/LOW/MIX, parallel to the synthetic Fig. 2.
Curves per panel:
- OFDMA [division], SFDMA [feature div.], NOMA-SIC (analytical baselines)
- UWCA (analytical) : oracle-beta cross-attention (relevance SUPPLIED) -- dotted
- UWCA w/o MAML : decoder TRAINED on real digits, no meta-learning (from realdata_train.json)
- UWCA w/ MAML : decoder TRAINED on real digits with MAML (proposed) -- hollow circles
Trained curves are read from results/realdata_train.json (produced by
revision_realdata_train.py); the analytical / baseline curves are recomputed here
so they share one Monte-Carlo setting. Saves results/fig_realdata_c.pdf.
"""
import json, numpy as np
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
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)
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)
def _norm(E): return E / (np.linalg.norm(E, axis=-1, keepdims=True) + 1e-8)
def se_channel(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)
nstd = np.sqrt(float(np.mean(Ytx**2))/(10**(snr/10)))
return h*Ytx[:, None, :] + RNG.standard_normal((n, U, D))*nstd
def ofdma(Y): return np.stack([_norm(Y[:, u, :]*MASKS[u]) for u in range(U)], 1)
def sfdma(E, snr):
"""Full-band, orthogonal semantic-subspace division (block basis)."""
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)
X_ = E * MASKS[None]
yv = (h * X_).sum(1)
yv = yv + RNG.standard_normal((n, D))*np.sqrt(float(np.mean(yv**2))/(10**(snr/10)))
return np.stack([_norm((yv * MASKS[u])/(h[:, u, :]+1e-8)) for u in range(U)], 1)
def noma_ch(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)))
return yv, h
def noma_sic(yv, h):
n = yv.shape[0]; Eh = np.zeros((n, U, D)); res = yv.copy()
for u in range(U):
Eh[:, u, :] = _norm(res/(h[:, u, :]+1e-8)); res -= h[:, u, :]*np.sqrt(NOMA_POWER[u])*Eh[:, u, :]
return Eh
def uwca_oracle(Y, bmc):
"""Analytical UWCA: oracle-beta cross-attention (relevance supplied)."""
R = Y[:, :, None, :]*MASKS[None, None]
a = bmc.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(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))
# panel tags quote the exact full-dataset mean pairwise cosines used in
# the manuscript text (0.427, -0.050, 0.064), not a Monte-Carlo resample
betas = {'HIGH': 0.427, 'LOW': -0.050, 'MIX': 0.064}
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='OMA')
a.plot(SNR, ana[s]['SFDMA'], 'v:', color=COL['SFDMA'], lw=2, ms=5, mfc='none', label='SFDMA')
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 (genie)')
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')
import json as _json
with open('results/realdata_analytic.json','w') as _f:
_json.dump({sc:{k:list(map(float,v)) for k,v in d.items()} for sc,d in ana.items()}, _f, indent=1)
print('saved results/realdata_analytic.json')
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}")
simulation/results/data/ablation.csv
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S,ser
1,0.649078125
2,0.4981796875
3,0.439421875
5,0.4009296875
7,0.394171875
10,0.39328125
15,0.3929921875
999,0.3908125
1 S ser
2 1 0.649078125
3 2 0.4981796875
4 3 0.439421875
5 5 0.4009296875
6 7 0.394171875
7 10 0.39328125
8 15 0.3929921875
9 999 0.3908125
simulation/results/data/attn_heatmaps.csv
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scenario,row,col,alpha
HIGH,0,0,0.45402950985684404
HIGH,0,1,0.19182746791451777
HIGH,0,2,0.17707150884416958
HIGH,0,3,0.17707150884416958
HIGH,1,0,0.19182746791451777
HIGH,1,1,0.45402950985684404
HIGH,1,2,0.17707150884416958
HIGH,1,3,0.17707150884416958
HIGH,2,0,0.18224298980260298
HIGH,2,1,0.18224298980260298
HIGH,2,2,0.4672897174425754
HIGH,2,3,0.16822429827932667
HIGH,3,0,0.18224298980260298
HIGH,3,1,0.18224298980260298
HIGH,3,2,0.16822429827932667
HIGH,3,3,0.4672897174425754
LOW,0,0,0.999999989999995
LOW,0,1,0.0
LOW,0,2,0.0
LOW,0,3,0.0
LOW,1,0,0.0
LOW,1,1,0.999999989999995
LOW,1,2,0.0
LOW,1,3,0.0
LOW,2,0,0.0
LOW,2,1,0.0
LOW,2,2,0.999999989999995
LOW,2,3,0.0
LOW,3,0,0.0
LOW,3,1,0.0
LOW,3,2,0.0
LOW,3,3,0.999999989999995
MIX,0,0,0.7029876927733757
MIX,0,1,0.2970123001967506
MIX,0,2,0.0
MIX,0,3,0.0
MIX,1,0,0.2970123001967506
MIX,1,1,0.7029876927733757
MIX,1,2,0.0
MIX,1,3,0.0
MIX,2,0,0.0
MIX,2,1,0.0
MIX,2,2,0.999999989999995
MIX,2,3,0.0
MIX,3,0,0.0
MIX,3,1,0.0
MIX,3,2,0.0
MIX,3,3,0.999999989999995
1 scenario row col alpha
2 HIGH 0 0 0.45402950985684404
3 HIGH 0 1 0.19182746791451777
4 HIGH 0 2 0.17707150884416958
5 HIGH 0 3 0.17707150884416958
6 HIGH 1 0 0.19182746791451777
7 HIGH 1 1 0.45402950985684404
8 HIGH 1 2 0.17707150884416958
9 HIGH 1 3 0.17707150884416958
10 HIGH 2 0 0.18224298980260298
11 HIGH 2 1 0.18224298980260298
12 HIGH 2 2 0.4672897174425754
13 HIGH 2 3 0.16822429827932667
14 HIGH 3 0 0.18224298980260298
15 HIGH 3 1 0.18224298980260298
16 HIGH 3 2 0.16822429827932667
17 HIGH 3 3 0.4672897174425754
18 LOW 0 0 0.999999989999995
19 LOW 0 1 0.0
20 LOW 0 2 0.0
21 LOW 0 3 0.0
22 LOW 1 0 0.0
23 LOW 1 1 0.999999989999995
24 LOW 1 2 0.0
25 LOW 1 3 0.0
26 LOW 2 0 0.0
27 LOW 2 1 0.0
28 LOW 2 2 0.999999989999995
29 LOW 2 3 0.0
30 LOW 3 0 0.0
31 LOW 3 1 0.0
32 LOW 3 2 0.0
33 LOW 3 3 0.999999989999995
34 MIX 0 0 0.7029876927733757
35 MIX 0 1 0.2970123001967506
36 MIX 0 2 0.0
37 MIX 0 3 0.0
38 MIX 1 0 0.2970123001967506
39 MIX 1 1 0.7029876927733757
40 MIX 1 2 0.0
41 MIX 1 3 0.0
42 MIX 2 0 0.0
43 MIX 2 1 0.0
44 MIX 2 2 0.999999989999995
45 MIX 2 3 0.0
46 MIX 3 0 0.0
47 MIX 3 1 0.0
48 MIX 3 2 0.0
49 MIX 3 3 0.999999989999995
simulation/results/data/beta_sweep.csv
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beta_sq,snr_label,ser_ofdma,ser_joint,ser_maml,gain_ofdma,gain_joint,gain_maml
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00134375
0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0025546875
0.0,10.0,0.0,0.0,0.0,0.0,0.0,-0.00115625
0.0025000000000000005,0.0,0.0,0.0,0.0,0.0,0.0,-0.002140625
0.0025000000000000005,5.0,0.0,0.0,0.0,0.0,0.0,0.000140625
0.0025000000000000005,10.0,0.0,0.0,0.0,0.0,0.0,-0.0011796875
0.010000000000000002,0.0,0.0,0.0,0.0,0.0,0.0,-0.000453125
0.010000000000000002,5.0,0.0,0.0,0.0,0.0,0.0,0.00128125
0.010000000000000002,10.0,0.0,0.0,0.0,0.0,0.0,0.00053125
0.022500000000000006,0.0,0.0,0.0,0.0,0.0,0.0,-0.0011015625
0.022500000000000006,5.0,0.0,0.0,0.0,0.0,0.0,0.000953125
0.022500000000000006,10.0,0.0,0.0,0.0,0.0,0.0,-0.00040625
0.04000000000000001,0.0,0.0,0.0,0.0,0.0,0.0,0.003375
0.04000000000000001,5.0,0.0,0.0,0.0,0.0,0.0,0.003484375
0.04000000000000001,10.0,0.0,0.0,0.0,0.0,0.0,0.0043984375
0.0625,0.0,0.0,0.0,0.0,0.0,0.0,0.002953125
0.0625,5.0,0.0,0.0,0.0,0.0,0.0,0.0067890625
0.0625,10.0,0.0,0.0,0.0,0.0,0.0,0.012421875
0.09000000000000002,0.0,0.0,0.0,0.0,0.0,0.0,0.00928125
0.09000000000000002,5.0,0.0,0.0,0.0,0.0,0.0,0.0155234375
0.09000000000000002,10.0,0.0,0.0,0.0,0.0,0.0,0.0261796875
0.12250000000000003,0.0,0.0,0.0,0.0,0.0,0.0,0.016921875
0.12250000000000003,5.0,0.0,0.0,0.0,0.0,0.0,0.0337265625
0.12250000000000003,10.0,0.0,0.0,0.0,0.0,0.0,0.0435859375
0.16000000000000003,0.0,0.0,0.0,0.0,0.0,0.0,0.03275
0.16000000000000003,5.0,0.0,0.0,0.0,0.0,0.0,0.0591328125
0.16000000000000003,10.0,0.0,0.0,0.0,0.0,0.0,0.0773046875
0.2025,0.0,0.0,0.0,0.0,0.0,0.0,0.049828125
0.2025,5.0,0.0,0.0,0.0,0.0,0.0,0.0895625
0.2025,10.0,0.0,0.0,0.0,0.0,0.0,0.1173359375
0.25,0.0,0.0,0.0,0.0,0.0,0.0,0.073390625
0.25,5.0,0.0,0.0,0.0,0.0,0.0,0.133765625
0.25,10.0,0.0,0.0,0.0,0.0,0.0,0.1698828125
0.30250000000000005,0.0,0.0,0.0,0.0,0.0,0.0,0.109625
0.30250000000000005,5.0,0.0,0.0,0.0,0.0,0.0,0.18753125
0.30250000000000005,10.0,0.0,0.0,0.0,0.0,0.0,0.22446875
0.3600000000000001,0.0,0.0,0.0,0.0,0.0,0.0,0.1545546875
0.3600000000000001,5.0,0.0,0.0,0.0,0.0,0.0,0.2577109375
0.3600000000000001,10.0,0.0,0.0,0.0,0.0,0.0,0.2910546875
0.42250000000000004,0.0,0.0,0.0,0.0,0.0,0.0,0.2079453125
0.42250000000000004,5.0,0.0,0.0,0.0,0.0,0.0,0.327125
0.42250000000000004,10.0,0.0,0.0,0.0,0.0,0.0,0.3514921875
0.4900000000000001,0.0,0.0,0.0,0.0,0.0,0.0,0.278296875
0.4900000000000001,5.0,0.0,0.0,0.0,0.0,0.0,0.3944375
0.4900000000000001,10.0,0.0,0.0,0.0,0.0,0.0,0.4004609375
0.5625,0.0,0.0,0.0,0.0,0.0,0.0,0.3422421875
0.5625,5.0,0.0,0.0,0.0,0.0,0.0,0.4500859375
0.5625,10.0,0.0,0.0,0.0,0.0,0.0,0.4313984375
0.6400000000000001,0.0,0.0,0.0,0.0,0.0,0.0,0.4103203125
0.6400000000000001,5.0,0.0,0.0,0.0,0.0,0.0,0.4968046875
0.6400000000000001,10.0,0.0,0.0,0.0,0.0,0.0,0.45440625
0.7225000000000001,0.0,0.0,0.0,0.0,0.0,0.0,0.469578125
0.7225000000000001,5.0,0.0,0.0,0.0,0.0,0.0,0.523203125
0.7225000000000001,10.0,0.0,0.0,0.0,0.0,0.0,0.47478125
0.81,0.0,0.0,0.0,0.0,0.0,0.0,0.5155078125
0.81,5.0,0.0,0.0,0.0,0.0,0.0,0.54571875
0.81,10.0,0.0,0.0,0.0,0.0,0.0,0.4928984375
1 beta_sq snr_label ser_ofdma ser_joint ser_maml gain_ofdma gain_joint gain_maml
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00134375
3 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0025546875
4 0.0 10.0 0.0 0.0 0.0 0.0 0.0 -0.00115625
5 0.0025000000000000005 0.0 0.0 0.0 0.0 0.0 0.0 -0.002140625
6 0.0025000000000000005 5.0 0.0 0.0 0.0 0.0 0.0 0.000140625
7 0.0025000000000000005 10.0 0.0 0.0 0.0 0.0 0.0 -0.0011796875
8 0.010000000000000002 0.0 0.0 0.0 0.0 0.0 0.0 -0.000453125
9 0.010000000000000002 5.0 0.0 0.0 0.0 0.0 0.0 0.00128125
10 0.010000000000000002 10.0 0.0 0.0 0.0 0.0 0.0 0.00053125
11 0.022500000000000006 0.0 0.0 0.0 0.0 0.0 0.0 -0.0011015625
12 0.022500000000000006 5.0 0.0 0.0 0.0 0.0 0.0 0.000953125
13 0.022500000000000006 10.0 0.0 0.0 0.0 0.0 0.0 -0.00040625
14 0.04000000000000001 0.0 0.0 0.0 0.0 0.0 0.0 0.003375
15 0.04000000000000001 5.0 0.0 0.0 0.0 0.0 0.0 0.003484375
16 0.04000000000000001 10.0 0.0 0.0 0.0 0.0 0.0 0.0043984375
17 0.0625 0.0 0.0 0.0 0.0 0.0 0.0 0.002953125
18 0.0625 5.0 0.0 0.0 0.0 0.0 0.0 0.0067890625
19 0.0625 10.0 0.0 0.0 0.0 0.0 0.0 0.012421875
20 0.09000000000000002 0.0 0.0 0.0 0.0 0.0 0.0 0.00928125
21 0.09000000000000002 5.0 0.0 0.0 0.0 0.0 0.0 0.0155234375
22 0.09000000000000002 10.0 0.0 0.0 0.0 0.0 0.0 0.0261796875
23 0.12250000000000003 0.0 0.0 0.0 0.0 0.0 0.0 0.016921875
24 0.12250000000000003 5.0 0.0 0.0 0.0 0.0 0.0 0.0337265625
25 0.12250000000000003 10.0 0.0 0.0 0.0 0.0 0.0 0.0435859375
26 0.16000000000000003 0.0 0.0 0.0 0.0 0.0 0.0 0.03275
27 0.16000000000000003 5.0 0.0 0.0 0.0 0.0 0.0 0.0591328125
28 0.16000000000000003 10.0 0.0 0.0 0.0 0.0 0.0 0.0773046875
29 0.2025 0.0 0.0 0.0 0.0 0.0 0.0 0.049828125
30 0.2025 5.0 0.0 0.0 0.0 0.0 0.0 0.0895625
31 0.2025 10.0 0.0 0.0 0.0 0.0 0.0 0.1173359375
32 0.25 0.0 0.0 0.0 0.0 0.0 0.0 0.073390625
33 0.25 5.0 0.0 0.0 0.0 0.0 0.0 0.133765625
34 0.25 10.0 0.0 0.0 0.0 0.0 0.0 0.1698828125
35 0.30250000000000005 0.0 0.0 0.0 0.0 0.0 0.0 0.109625
36 0.30250000000000005 5.0 0.0 0.0 0.0 0.0 0.0 0.18753125
37 0.30250000000000005 10.0 0.0 0.0 0.0 0.0 0.0 0.22446875
38 0.3600000000000001 0.0 0.0 0.0 0.0 0.0 0.0 0.1545546875
39 0.3600000000000001 5.0 0.0 0.0 0.0 0.0 0.0 0.2577109375
40 0.3600000000000001 10.0 0.0 0.0 0.0 0.0 0.0 0.2910546875
41 0.42250000000000004 0.0 0.0 0.0 0.0 0.0 0.0 0.2079453125
42 0.42250000000000004 5.0 0.0 0.0 0.0 0.0 0.0 0.327125
43 0.42250000000000004 10.0 0.0 0.0 0.0 0.0 0.0 0.3514921875
44 0.4900000000000001 0.0 0.0 0.0 0.0 0.0 0.0 0.278296875
45 0.4900000000000001 5.0 0.0 0.0 0.0 0.0 0.0 0.3944375
46 0.4900000000000001 10.0 0.0 0.0 0.0 0.0 0.0 0.4004609375
47 0.5625 0.0 0.0 0.0 0.0 0.0 0.0 0.3422421875
48 0.5625 5.0 0.0 0.0 0.0 0.0 0.0 0.4500859375
49 0.5625 10.0 0.0 0.0 0.0 0.0 0.0 0.4313984375
50 0.6400000000000001 0.0 0.0 0.0 0.0 0.0 0.0 0.4103203125
51 0.6400000000000001 5.0 0.0 0.0 0.0 0.0 0.0 0.4968046875
52 0.6400000000000001 10.0 0.0 0.0 0.0 0.0 0.0 0.45440625
53 0.7225000000000001 0.0 0.0 0.0 0.0 0.0 0.0 0.469578125
54 0.7225000000000001 5.0 0.0 0.0 0.0 0.0 0.0 0.523203125
55 0.7225000000000001 10.0 0.0 0.0 0.0 0.0 0.0 0.47478125
56 0.81 0.0 0.0 0.0 0.0 0.0 0.0 0.5155078125
57 0.81 5.0 0.0 0.0 0.0 0.0 0.0 0.54571875
58 0.81 10.0 0.0 0.0 0.0 0.0 0.0 0.4928984375
simulation/results/data/fair_comparison.csv
Executable
+67
View File
@@ -0,0 +1,67 @@
snr_db,U,method,ser
0.0,1,OFDMA,0.9928125
0.0,1,UWCA,0.9928125
2.0,1,OFDMA,0.97759375
2.0,1,UWCA,0.97759375
4.0,1,OFDMA,0.93403125
4.0,1,UWCA,0.93403125
6.0,1,OFDMA,0.8610625
6.0,1,UWCA,0.8610625
8.0,1,OFDMA,0.7381875
8.0,1,UWCA,0.7381875
10.0,1,OFDMA,0.59246875
10.0,1,UWCA,0.59246875
12.0,1,OFDMA,0.448375
12.0,1,UWCA,0.448375
14.0,1,OFDMA,0.3166875
14.0,1,UWCA,0.3166875
16.0,1,OFDMA,0.2171875
16.0,1,UWCA,0.2171875
18.0,1,OFDMA,0.14134375
18.0,1,UWCA,0.14134375
20.0,1,OFDMA,0.09734375
20.0,1,UWCA,0.09734375
0.0,2,OFDMA,0.99253125
0.0,2,UWCA,0.97284375
2.0,2,OFDMA,0.97678125
2.0,2,UWCA,0.930046875
4.0,2,OFDMA,0.939703125
4.0,2,UWCA,0.851828125
6.0,2,OFDMA,0.861546875
6.0,2,UWCA,0.733
8.0,2,OFDMA,0.7429375
8.0,2,UWCA,0.58740625
10.0,2,OFDMA,0.591125
10.0,2,UWCA,0.4421875
12.0,2,OFDMA,0.445375
12.0,2,UWCA,0.317453125
14.0,2,OFDMA,0.3173125
14.0,2,UWCA,0.216140625
16.0,2,OFDMA,0.21378125
16.0,2,UWCA,0.142546875
18.0,2,OFDMA,0.143828125
18.0,2,UWCA,0.095046875
20.0,2,OFDMA,0.09346875
20.0,2,UWCA,0.060640625
0.0,4,OFDMA,0.993
0.0,4,UWCA,0.9138203125
2.0,4,OFDMA,0.9770703125
2.0,4,UWCA,0.8270546875
4.0,4,OFDMA,0.9386953125
4.0,4,UWCA,0.7049140625
6.0,4,OFDMA,0.8601796875
6.0,4,UWCA,0.5569765625
8.0,4,OFDMA,0.7411171875
8.0,4,UWCA,0.41609375
10.0,4,OFDMA,0.594765625
10.0,4,UWCA,0.297515625
12.0,4,OFDMA,0.444359375
12.0,4,UWCA,0.202
14.0,4,OFDMA,0.317171875
14.0,4,UWCA,0.13403125
16.0,4,OFDMA,0.2164609375
16.0,4,UWCA,0.088765625
18.0,4,OFDMA,0.1434765625
18.0,4,UWCA,0.0565859375
20.0,4,OFDMA,0.094890625
20.0,4,UWCA,0.0358828125
1 snr_db U method ser
2 0.0 1 OFDMA 0.9928125
3 0.0 1 UWCA 0.9928125
4 2.0 1 OFDMA 0.97759375
5 2.0 1 UWCA 0.97759375
6 4.0 1 OFDMA 0.93403125
7 4.0 1 UWCA 0.93403125
8 6.0 1 OFDMA 0.8610625
9 6.0 1 UWCA 0.8610625
10 8.0 1 OFDMA 0.7381875
11 8.0 1 UWCA 0.7381875
12 10.0 1 OFDMA 0.59246875
13 10.0 1 UWCA 0.59246875
14 12.0 1 OFDMA 0.448375
15 12.0 1 UWCA 0.448375
16 14.0 1 OFDMA 0.3166875
17 14.0 1 UWCA 0.3166875
18 16.0 1 OFDMA 0.2171875
19 16.0 1 UWCA 0.2171875
20 18.0 1 OFDMA 0.14134375
21 18.0 1 UWCA 0.14134375
22 20.0 1 OFDMA 0.09734375
23 20.0 1 UWCA 0.09734375
24 0.0 2 OFDMA 0.99253125
25 0.0 2 UWCA 0.97284375
26 2.0 2 OFDMA 0.97678125
27 2.0 2 UWCA 0.930046875
28 4.0 2 OFDMA 0.939703125
29 4.0 2 UWCA 0.851828125
30 6.0 2 OFDMA 0.861546875
31 6.0 2 UWCA 0.733
32 8.0 2 OFDMA 0.7429375
33 8.0 2 UWCA 0.58740625
34 10.0 2 OFDMA 0.591125
35 10.0 2 UWCA 0.4421875
36 12.0 2 OFDMA 0.445375
37 12.0 2 UWCA 0.317453125
38 14.0 2 OFDMA 0.3173125
39 14.0 2 UWCA 0.216140625
40 16.0 2 OFDMA 0.21378125
41 16.0 2 UWCA 0.142546875
42 18.0 2 OFDMA 0.143828125
43 18.0 2 UWCA 0.095046875
44 20.0 2 OFDMA 0.09346875
45 20.0 2 UWCA 0.060640625
46 0.0 4 OFDMA 0.993
47 0.0 4 UWCA 0.9138203125
48 2.0 4 OFDMA 0.9770703125
49 2.0 4 UWCA 0.8270546875
50 4.0 4 OFDMA 0.9386953125
51 4.0 4 UWCA 0.7049140625
52 6.0 4 OFDMA 0.8601796875
53 6.0 4 UWCA 0.5569765625
54 8.0 4 OFDMA 0.7411171875
55 8.0 4 UWCA 0.41609375
56 10.0 4 OFDMA 0.594765625
57 10.0 4 UWCA 0.297515625
58 12.0 4 OFDMA 0.444359375
59 12.0 4 UWCA 0.202
60 14.0 4 OFDMA 0.317171875
61 14.0 4 UWCA 0.13403125
62 16.0 4 OFDMA 0.2164609375
63 16.0 4 UWCA 0.088765625
64 18.0 4 OFDMA 0.1434765625
65 18.0 4 UWCA 0.0565859375
66 20.0 4 OFDMA 0.094890625
67 20.0 4 UWCA 0.0358828125
simulation/results/data/mi_bounds.csv
Executable
+801
View File
@@ -0,0 +1,801 @@
snr_db,U,I_ofdma,I_uwca,ratio_low_snr
0.0,1,1.4208262673459844,1.4208262673459844,1.0
0.10050251256281408,1,1.453581613230981,1.453581613230981,1.0
0.20100502512562815,1,1.4870804648006348,1.4870804648006348,1.0
0.30150753768844224,1,1.5213391729333086,1.5213391729333086,1.0
0.4020100502512563,1,1.5563744246863505,1.5563744246863505,1.0
0.5025125628140704,1,1.5922032491823384,1.5922032491823384,1.0
0.6030150753768845,1,1.6288430235535298,1.6288430235535298,1.0
0.7035175879396985,1,1.66631147894303,1.66631147894303,1.0
0.8040201005025126,1,1.7046267065610003,1.7046267065610003,1.0
0.9045226130653267,1,1.7438071637941857,1.7438071637941857,1.0
1.0050251256281408,1,1.7838716803668317,1.7838716803668317,1.0
1.105527638190955,1,1.8248394645509993,1.8248394645509993,1.0
1.206030150753769,1,1.8667301094240936,1.8667301094240936,1.0
1.306532663316583,1,1.9095635991713202,1.9095635991713202,1.0
1.407035175879397,1,1.9533603154306116,1.9533603154306116,1.0
1.5075376884422111,1,1.9981410436774405,1.9981410436774405,1.0
1.6080402010050252,1,2.0439269796467525,2.0439269796467525,1.0
1.7085427135678393,1,2.0907397357891377,2.0907397357891377,1.0
1.8090452261306533,1,2.1386013477581476,2.1386013477581476,1.0
1.9095477386934674,1,2.18753428092554,2.18753428092554,1.0
2.0100502512562817,1,2.2375614369210455,2.2375614369210455,1.0
2.1105527638190957,1,2.2887061601930765,2.2887061601930765,1.0
2.21105527638191,1,2.3409922445866282,2.3409922445866282,1.0
2.311557788944724,1,2.394443939934439,2.394443939934439,1.0
2.412060301507538,1,2.4490859586572906,2.4490859586572906,1.0
2.512562814070352,1,2.5049434823691383,2.5049434823691383,1.0
2.613065326633166,1,2.5620421684825763,2.5620421684825763,1.0
2.71356783919598,1,2.620408156809956,2.620408156809956,1.0
2.814070351758794,1,2.6800680761552433,2.6800680761552433,1.0
2.9145728643216082,1,2.741049050891556,2.741049050891556,1.0
3.0150753768844223,1,2.8033787075190664,2.8033787075190664,1.0
3.1155778894472363,1,2.867085181197777,2.867085181197777,1.0
3.2160804020100504,1,2.9321971222494754,2.9321971222494754,1.0
3.3165829145728645,1,2.9987437026229338,2.9987437026229338,1.0
3.4170854271356785,1,3.066754622316253,3.066754622316253,1.0
3.5175879396984926,1,3.1362601157499874,3.1362601157499874,1.0
3.6180904522613067,1,3.2072909580845184,3.2072909580845184,1.0
3.7185929648241207,1,3.279878471474911,3.279878471474911,1.0
3.819095477386935,1,3.354054531256265,3.354054531256265,1.0
3.919597989949749,1,3.4298515720523746,3.4298515720523746,1.0
4.020100502512563,1,3.5073025938002855,3.5073025938002855,1.0
4.120603015075377,1,3.5864411676831356,3.5864411676831356,1.0
4.2211055276381915,1,3.667301441963448,3.667301441963448,1.0
4.3216080402010055,1,3.749918147708827,3.749918147708827,1.0
4.42211055276382,1,3.8343266044018085,3.8343266044018085,1.0
4.522613065326634,1,3.9205627254254307,3.9205627254254307,1.0
4.623115577889448,1,4.0086630234158305,4.0086630234158305,1.0
4.723618090452262,1,4.098664615473057,4.098664615473057,1.0
4.824120603015076,1,4.190605228221021,4.190605228221021,1.0
4.92462311557789,1,4.284523202707354,4.284523202707354,1.0
5.025125628140704,1,4.380457499133756,4.380457499133756,1.0
5.125628140703518,1,4.478447701407246,4.478447701407246,1.0
5.226130653266332,1,4.578534021502519,4.578534021502519,1.0
5.326633165829146,1,4.680757303625506,4.680757303625506,1.0
5.42713567839196,1,4.785159028168034,4.785159028168034,1.0
5.527638190954774,1,4.8917813154433505,4.8917813154433505,1.0
5.628140703517588,1,5.000666929192141,5.000666929192141,1.0
5.728643216080402,1,5.111859279848548,5.111859279848548,1.0
5.8291457286432165,1,5.225402427555554,5.225402427555554,1.0
5.9296482412060305,1,5.341341084919024,5.341341084919024,1.0
6.030150753768845,1,5.4597206194895636,5.4597206194895636,1.0
6.130653266331659,1,5.580587055961325,5.580587055961325,1.0
6.231155778894473,1,5.70398707807676,5.70398707807676,1.0
6.331658291457287,1,5.829968030226323,5.829968030226323,1.0
6.432160804020101,1,5.958577918732027,5.958577918732027,1.0
6.532663316582915,1,6.089865412803814,6.089865412803814,1.0
6.633165829145729,1,6.223879845157607,6.223879845157607,1.0
6.733668341708543,1,6.360671212283967,6.360671212283967,1.0
6.834170854271357,1,6.500290174356296,6.500290174356296,1.0
6.934673366834171,1,6.642788054767577,6.642788054767577,1.0
7.035175879396985,1,6.788216839284699,6.788216839284699,1.0
7.135678391959799,1,6.936629174809471,6.936629174809471,1.0
7.236180904522613,1,7.088078367735592,7.088078367735592,1.0
7.336683417085427,1,7.2426183818909005,7.2426183818909005,1.0
7.437185929648241,1,7.4003038360544116,7.4003038360544116,1.0
7.5376884422110555,1,7.561190001037832,7.561190001037832,1.0
7.63819095477387,1,7.725332796321313,7.725332796321313,1.0
7.738693467336684,1,7.892788786233608,7.892788786233608,1.0
7.839195979899498,1,8.063615175666836,8.063615175666836,1.0
7.939698492462312,1,8.237869805316436,8.237869805316436,1.0
8.040201005025127,1,8.415611146437106,8.415611146437106,1.0
8.14070351758794,1,8.596898295105877,8.596898295105877,1.0
8.241206030150755,1,8.781790965983706,8.781790965983706,1.0
8.341708542713569,1,8.970349485567462,8.970349485567462,1.0
8.442211055276383,1,9.162634784924379,9.162634784924379,1.0
8.542713567839197,1,9.358708391901606,9.358708391901606,1.0
8.643216080402011,1,9.558632422803802,9.558632422803802,1.0
8.743718592964825,1,9.762469573532194,9.762469573532194,1.0
8.84422110552764,1,9.970283110178976,9.970283110178976,1.0
8.944723618090453,1,10.182136859071488,10.182136859071488,1.0
9.045226130653267,1,10.398095196260991,10.398095196260991,1.0
9.145728643216081,1,10.618223036451502,10.618223036451502,1.0
9.246231155778895,1,10.842585821364706,10.842585821364706,1.0
9.34673366834171,1,11.071249507537441,11.071249507537441,1.0
9.447236180904524,1,11.304280553549004,11.304280553549004,1.0
9.547738693467338,1,11.541745906675992,11.541745906675992,1.0
9.648241206030152,1,11.78371298897318,11.78371298897318,1.0
9.748743718592966,1,12.03024968277949,12.03024968277949,1.0
9.84924623115578,1,12.2814243156488,12.2814243156488,1.0
9.949748743718594,1,12.537305644706167,12.537305644706167,1.0
10.050251256281408,1,12.797962840430452,12.797962840430452,1.0
10.150753768844222,1,13.063465469865516,13.063465469865516,1.0
10.251256281407036,1,13.333883479262356,13.333883479262356,1.0
10.35175879396985,1,13.609287176155831,13.609287176155831,1.0
10.452261306532664,1,13.889747210879996,13.889747210879996,1.0
10.552763819095478,1,14.175334557527044,14.175334557527044,1.0
10.653266331658292,1,14.466120494355588,14.466120494355588,1.0
10.753768844221106,1,14.762176583654847,14.762176583654847,1.0
10.85427135678392,1,15.063574651072,15.063574651072,1.0
10.954773869346734,1,15.370386764410869,15.370386764410869,1.0
11.055276381909549,1,15.682685211910876,15.682685211910876,1.0
11.155778894472363,1,16.000542480016072,16.000542480016072,1.0
11.256281407035177,1,16.324031230644675,16.324031230644675,1.0
11.35678391959799,1,16.65322427797064,16.65322427797064,1.0
11.457286432160805,1,16.98819456472927,16.98819456472927,1.0
11.557788944723619,1,17.329015138059958,17.329015138059958,1.0
11.658291457286433,1,17.675759124899773,17.675759124899773,1.0
11.758793969849247,1,18.028499706942466,18.028499706942466,1.0
11.859296482412061,1,18.387310095178158,18.387310095178158,1.0
11.959798994974875,1,18.752263504029788,18.752263504029788,1.0
12.06030150753769,1,19.123433125103254,19.123433125103254,1.0
12.160804020100503,1,19.500892100568656,19.500892100568656,1.0
12.261306532663317,1,19.884713496190994,19.884713496190994,1.0
12.361809045226131,1,20.274970274029318,20.274970274029318,1.0
12.462311557788945,1,20.67173526482383,20.67173526482383,1.0
12.56281407035176,1,21.075081140091402,21.075081140091402,1.0
12.663316582914574,1,21.4850803839503,21.4850803839503,1.0
12.763819095477388,1,21.901805264695717,21.901805264695717,1.0
12.864321608040202,1,22.325327806148156,22.325327806148156,1.0
12.964824120603016,1,22.75571975879734,22.75571975879734,1.0
13.06532663316583,1,23.1930525707648,23.1930525707648,1.0
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18.592964824120603,4,14.938529775677651,52.82278980457108,3.7075
18.69346733668342,4,15.168008824822838,53.59248410159894,3.7075
18.79396984924623,4,15.399895508562977,54.36890208210218,3.7075
18.894472361809047,4,15.634192565702666,55.15200750489372,3.7075
18.99497487437186,4,15.870902366632725,55.94176223046943,3.7075
19.095477386934675,4,16.110026913393924,56.738126228898494,3.7075
19.195979899497488,4,16.35156783994252,57.54105758893916,3.7075
19.296482412060303,4,16.595526412616323,58.3505125283777,3.7075
19.396984924623116,4,16.841903530799065,59.16644540558462,3.7075
19.49748743718593,4,17.09069972778146,59.98880873228424,3.7075
19.597989949748744,4,17.341915171816225,60.81755318752846,3.7075
19.69849246231156,4,17.595549667365283,61.6526276328689,3.7075
19.798994974874372,4,17.85160265653593,62.49397912871611,3.7075
19.899497487437188,4,18.11007322070373,63.34155295187665,3.7075
20.0,4,18.37096008231871,64.19529261425491,3.7075
1 snr_db U I_ofdma I_uwca ratio_low_snr
2 0.0 1 1.4208262673459844 1.4208262673459844 1.0
3 0.10050251256281408 1 1.453581613230981 1.453581613230981 1.0
4 0.20100502512562815 1 1.4870804648006348 1.4870804648006348 1.0
5 0.30150753768844224 1 1.5213391729333086 1.5213391729333086 1.0
6 0.4020100502512563 1 1.5563744246863505 1.5563744246863505 1.0
7 0.5025125628140704 1 1.5922032491823384 1.5922032491823384 1.0
8 0.6030150753768845 1 1.6288430235535298 1.6288430235535298 1.0
9 0.7035175879396985 1 1.66631147894303 1.66631147894303 1.0
10 0.8040201005025126 1 1.7046267065610003 1.7046267065610003 1.0
11 0.9045226130653267 1 1.7438071637941857 1.7438071637941857 1.0
12 1.0050251256281408 1 1.7838716803668317 1.7838716803668317 1.0
13 1.105527638190955 1 1.8248394645509993 1.8248394645509993 1.0
14 1.206030150753769 1 1.8667301094240936 1.8667301094240936 1.0
15 1.306532663316583 1 1.9095635991713202 1.9095635991713202 1.0
16 1.407035175879397 1 1.9533603154306116 1.9533603154306116 1.0
17 1.5075376884422111 1 1.9981410436774405 1.9981410436774405 1.0
18 1.6080402010050252 1 2.0439269796467525 2.0439269796467525 1.0
19 1.7085427135678393 1 2.0907397357891377 2.0907397357891377 1.0
20 1.8090452261306533 1 2.1386013477581476 2.1386013477581476 1.0
21 1.9095477386934674 1 2.18753428092554 2.18753428092554 1.0
22 2.0100502512562817 1 2.2375614369210455 2.2375614369210455 1.0
23 2.1105527638190957 1 2.2887061601930765 2.2887061601930765 1.0
24 2.21105527638191 1 2.3409922445866282 2.3409922445866282 1.0
25 2.311557788944724 1 2.394443939934439 2.394443939934439 1.0
26 2.412060301507538 1 2.4490859586572906 2.4490859586572906 1.0
27 2.512562814070352 1 2.5049434823691383 2.5049434823691383 1.0
28 2.613065326633166 1 2.5620421684825763 2.5620421684825763 1.0
29 2.71356783919598 1 2.620408156809956 2.620408156809956 1.0
30 2.814070351758794 1 2.6800680761552433 2.6800680761552433 1.0
31 2.9145728643216082 1 2.741049050891556 2.741049050891556 1.0
32 3.0150753768844223 1 2.8033787075190664 2.8033787075190664 1.0
33 3.1155778894472363 1 2.867085181197777 2.867085181197777 1.0
34 3.2160804020100504 1 2.9321971222494754 2.9321971222494754 1.0
35 3.3165829145728645 1 2.9987437026229338 2.9987437026229338 1.0
36 3.4170854271356785 1 3.066754622316253 3.066754622316253 1.0
37 3.5175879396984926 1 3.1362601157499874 3.1362601157499874 1.0
38 3.6180904522613067 1 3.2072909580845184 3.2072909580845184 1.0
39 3.7185929648241207 1 3.279878471474911 3.279878471474911 1.0
40 3.819095477386935 1 3.354054531256265 3.354054531256265 1.0
41 3.919597989949749 1 3.4298515720523746 3.4298515720523746 1.0
42 4.020100502512563 1 3.5073025938002855 3.5073025938002855 1.0
43 4.120603015075377 1 3.5864411676831356 3.5864411676831356 1.0
44 4.2211055276381915 1 3.667301441963448 3.667301441963448 1.0
45 4.3216080402010055 1 3.749918147708827 3.749918147708827 1.0
46 4.42211055276382 1 3.8343266044018085 3.8343266044018085 1.0
47 4.522613065326634 1 3.9205627254254307 3.9205627254254307 1.0
48 4.623115577889448 1 4.0086630234158305 4.0086630234158305 1.0
49 4.723618090452262 1 4.098664615473057 4.098664615473057 1.0
50 4.824120603015076 1 4.190605228221021 4.190605228221021 1.0
51 4.92462311557789 1 4.284523202707354 4.284523202707354 1.0
52 5.025125628140704 1 4.380457499133756 4.380457499133756 1.0
53 5.125628140703518 1 4.478447701407246 4.478447701407246 1.0
54 5.226130653266332 1 4.578534021502519 4.578534021502519 1.0
55 5.326633165829146 1 4.680757303625506 4.680757303625506 1.0
56 5.42713567839196 1 4.785159028168034 4.785159028168034 1.0
57 5.527638190954774 1 4.8917813154433505 4.8917813154433505 1.0
58 5.628140703517588 1 5.000666929192141 5.000666929192141 1.0
59 5.728643216080402 1 5.111859279848548 5.111859279848548 1.0
60 5.8291457286432165 1 5.225402427555554 5.225402427555554 1.0
61 5.9296482412060305 1 5.341341084919024 5.341341084919024 1.0
62 6.030150753768845 1 5.4597206194895636 5.4597206194895636 1.0
63 6.130653266331659 1 5.580587055961325 5.580587055961325 1.0
64 6.231155778894473 1 5.70398707807676 5.70398707807676 1.0
65 6.331658291457287 1 5.829968030226323 5.829968030226323 1.0
66 6.432160804020101 1 5.958577918732027 5.958577918732027 1.0
67 6.532663316582915 1 6.089865412803814 6.089865412803814 1.0
68 6.633165829145729 1 6.223879845157607 6.223879845157607 1.0
69 6.733668341708543 1 6.360671212283967 6.360671212283967 1.0
70 6.834170854271357 1 6.500290174356296 6.500290174356296 1.0
71 6.934673366834171 1 6.642788054767577 6.642788054767577 1.0
72 7.035175879396985 1 6.788216839284699 6.788216839284699 1.0
73 7.135678391959799 1 6.936629174809471 6.936629174809471 1.0
74 7.236180904522613 1 7.088078367735592 7.088078367735592 1.0
75 7.336683417085427 1 7.2426183818909005 7.2426183818909005 1.0
76 7.437185929648241 1 7.4003038360544116 7.4003038360544116 1.0
77 7.5376884422110555 1 7.561190001037832 7.561190001037832 1.0
78 7.63819095477387 1 7.725332796321313 7.725332796321313 1.0
79 7.738693467336684 1 7.892788786233608 7.892788786233608 1.0
80 7.839195979899498 1 8.063615175666836 8.063615175666836 1.0
81 7.939698492462312 1 8.237869805316436 8.237869805316436 1.0
82 8.040201005025127 1 8.415611146437106 8.415611146437106 1.0
83 8.14070351758794 1 8.596898295105877 8.596898295105877 1.0
84 8.241206030150755 1 8.781790965983706 8.781790965983706 1.0
85 8.341708542713569 1 8.970349485567462 8.970349485567462 1.0
86 8.442211055276383 1 9.162634784924379 9.162634784924379 1.0
87 8.542713567839197 1 9.358708391901606 9.358708391901606 1.0
88 8.643216080402011 1 9.558632422803802 9.558632422803802 1.0
89 8.743718592964825 1 9.762469573532194 9.762469573532194 1.0
90 8.84422110552764 1 9.970283110178976 9.970283110178976 1.0
91 8.944723618090453 1 10.182136859071488 10.182136859071488 1.0
92 9.045226130653267 1 10.398095196260991 10.398095196260991 1.0
93 9.145728643216081 1 10.618223036451502 10.618223036451502 1.0
94 9.246231155778895 1 10.842585821364706 10.842585821364706 1.0
95 9.34673366834171 1 11.071249507537441 11.071249507537441 1.0
96 9.447236180904524 1 11.304280553549004 11.304280553549004 1.0
97 9.547738693467338 1 11.541745906675992 11.541745906675992 1.0
98 9.648241206030152 1 11.78371298897318 11.78371298897318 1.0
99 9.748743718592966 1 12.03024968277949 12.03024968277949 1.0
100 9.84924623115578 1 12.2814243156488 12.2814243156488 1.0
101 9.949748743718594 1 12.537305644706167 12.537305644706167 1.0
102 10.050251256281408 1 12.797962840430452 12.797962840430452 1.0
103 10.150753768844222 1 13.063465469865516 13.063465469865516 1.0
104 10.251256281407036 1 13.333883479262356 13.333883479262356 1.0
105 10.35175879396985 1 13.609287176155831 13.609287176155831 1.0
106 10.452261306532664 1 13.889747210879996 13.889747210879996 1.0
107 10.552763819095478 1 14.175334557527044 14.175334557527044 1.0
108 10.653266331658292 1 14.466120494355588 14.466120494355588 1.0
109 10.753768844221106 1 14.762176583654847 14.762176583654847 1.0
110 10.85427135678392 1 15.063574651072 15.063574651072 1.0
111 10.954773869346734 1 15.370386764410869 15.370386764410869 1.0
112 11.055276381909549 1 15.682685211910876 15.682685211910876 1.0
113 11.155778894472363 1 16.000542480016072 16.000542480016072 1.0
114 11.256281407035177 1 16.324031230644675 16.324031230644675 1.0
115 11.35678391959799 1 16.65322427797064 16.65322427797064 1.0
116 11.457286432160805 1 16.98819456472927 16.98819456472927 1.0
117 11.557788944723619 1 17.329015138059958 17.329015138059958 1.0
118 11.658291457286433 1 17.675759124899773 17.675759124899773 1.0
119 11.758793969849247 1 18.028499706942466 18.028499706942466 1.0
120 11.859296482412061 1 18.387310095178158 18.387310095178158 1.0
121 11.959798994974875 1 18.752263504029788 18.752263504029788 1.0
122 12.06030150753769 1 19.123433125103254 19.123433125103254 1.0
123 12.160804020100503 1 19.500892100568656 19.500892100568656 1.0
124 12.261306532663317 1 19.884713496190994 19.884713496190994 1.0
125 12.361809045226131 1 20.274970274029318 20.274970274029318 1.0
126 12.462311557788945 1 20.67173526482383 20.67173526482383 1.0
127 12.56281407035176 1 21.075081140091402 21.075081140091402 1.0
128 12.663316582914574 1 21.4850803839503 21.4850803839503 1.0
129 12.763819095477388 1 21.901805264695717 21.901805264695717 1.0
130 12.864321608040202 1 22.325327806148156 22.325327806148156 1.0
131 12.964824120603016 1 22.75571975879734 22.75571975879734 1.0
132 13.06532663316583 1 23.1930525707648 23.1930525707648 1.0
133 13.165829145728644 1 23.637397358608805 23.637397358608805 1.0
134 13.266331658291458 1 24.08882487799571 24.08882487799571 1.0
135 13.366834170854272 1 24.547405494262286 24.547405494262286 1.0
136 13.467336683417086 1 25.01320915289387 25.01320915289387 1.0
137 13.5678391959799 1 25.486305349943684 25.486305349943684 1.0
138 13.668341708542714 1 25.966763102418785 25.966763102418785 1.0
139 13.768844221105528 1 26.454650918658597 26.454650918658597 1.0
140 13.869346733668342 1 26.950036768732126 26.950036768732126 1.0
141 13.969849246231156 1 27.45298805487981 27.45298805487981 1.0
142 14.07035175879397 1 27.963571582026976 27.963571582026976 1.0
143 14.170854271356784 1 28.48185352839502 28.48185352839502 1.0
144 14.271356783919598 1 29.007899416237116 29.007899416237116 1.0
145 14.371859296482413 1 29.541774082725055 29.541774082725055 1.0
146 14.472361809045227 1 30.083541651013885 30.083541651013885 1.0
147 14.57286432160804 1 30.63326550151097 30.63326550151097 1.0
148 14.673366834170855 1 31.191008243375986 31.191008243375986 1.0
149 14.773869346733669 1 31.756831686278055 31.756831686278055 1.0
150 14.874371859296483 1 32.33079681243664 32.33079681243664 1.0
151 14.974874371859297 1 32.91296374897157 32.91296374897157 1.0
152 15.075376884422111 1 33.5033917405886 33.5033917405886 1.0
153 15.175879396984925 1 34.102139122625296 34.102139122625296 1.0
154 15.27638190954774 1 34.709263294482945 34.709263294482945 1.0
155 15.376884422110553 1 35.32482069346871 35.32482069346871 1.0
156 15.477386934673367 1 35.948866769072566 35.948866769072566 1.0
157 15.577889447236181 1 36.58145595770296 36.58145595770296 1.0
158 15.678391959798995 1 37.22264165790435 37.22264165790435 1.0
159 15.77889447236181 1 37.872476206079476 37.872476206079476 1.0
160 15.879396984924623 1 38.531010852739016 38.531010852739016 1.0
161 15.979899497487438 1 39.198295739299645 39.198295739299645 1.0
162 16.080402010050253 1 39.874379875452206 39.874379875452206 1.0
163 16.180904522613066 1 40.55931111711997 40.55931111711997 1.0
164 16.28140703517588 1 41.25313614502703 41.25313614502703 1.0
165 16.381909547738694 1 41.95590044389549 41.95590044389549 1.0
166 16.48241206030151 1 42.667648282289946 42.667648282289946 1.0
167 16.582914572864322 1 43.38842269312671 43.38842269312671 1.0
168 16.683417085427138 1 44.118265454864556 44.118265454864556 1.0
169 16.78391959798995 1 44.857217073392555 44.857217073392555 1.0
170 16.884422110552766 1 45.605316764630636 45.605316764630636 1.0
171 16.984924623115578 1 46.362602437856395 46.362602437856395 1.0
172 17.085427135678394 1 47.129110679772225 47.129110679772225 1.0
173 17.185929648241206 1 47.90487673932443 47.90487673932443 1.0
174 17.286432160804022 1 48.68993451328682 48.68993451328682 1.0
175 17.386934673366834 1 49.48431653261843 49.48431653261843 1.0
176 17.48743718592965 1 50.28805394960599 50.28805394960599 1.0
177 17.587939698492463 1 51.10117652579919 51.10117652579919 1.0
178 17.68844221105528 1 51.923712620747494 51.923712620747494 1.0
179 17.78894472361809 1 52.75568918154432 52.75568918154432 1.0
180 17.889447236180906 1 53.59713173318574 53.59713173318574 1.0
181 17.98994974874372 1 54.44806436974779 54.44806436974779 1.0
182 18.090452261306535 1 55.30850974638734 55.30850974638734 1.0
183 18.190954773869347 1 56.17848907216861 56.17848907216861 1.0
184 18.291457286432163 1 57.05802210371922 57.05802210371922 1.0
185 18.391959798994975 1 57.94712713971494 57.94712713971494 1.0
186 18.49246231155779 1 58.84582101619574 58.84582101619574 1.0
187 18.592964824120603 1 59.754119102710604 59.754119102710604 1.0
188 18.69346733668342 1 60.67203529929135 60.67203529929135 1.0
189 18.79396984924623 1 61.59958203425191 61.59958203425191 1.0
190 18.894472361809047 1 62.53677026281066 62.53677026281066 1.0
191 18.99497487437186 1 63.4836094665309 63.4836094665309 1.0
192 19.095477386934675 1 64.4401076535757 64.4401076535757 1.0
193 19.195979899497488 1 65.40627135977007 65.40627135977007 1.0
194 19.296482412060303 1 66.38210565046529 66.38210565046529 1.0
195 19.396984924623116 1 67.36761412319626 67.36761412319626 1.0
196 19.49748743718593 1 68.36279891112584 68.36279891112584 1.0
197 19.597989949748744 1 69.3676606872649 69.3676606872649 1.0
198 19.69849246231156 1 70.38219866946113 70.38219866946113 1.0
199 19.798994974874372 1 71.40641062614372 71.40641062614372 1.0
200 19.899497487437188 1 72.44029288281492 72.44029288281492 1.0
201 20.0 1 73.48384032927484 73.48384032927484 1.0
202 0.0 2 0.7104131336729922 1.350601704593459 1.9024999999999999
203 0.10050251256281408 2 0.7267908066154904 1.3817155464357236 1.9024999999999999
204 0.20100502512562815 2 0.7435402324003174 1.4135345845324436 1.9024999999999999
205 0.30150753768844224 2 0.7606695864666543 1.4460742785644207 1.9024999999999999
206 0.4020100502512563 2 0.7781872123431752 1.4793504037943648 1.9024999999999999
207 0.5025125628140704 2 0.7961016245911692 1.5133790564834446 1.9024999999999999
208 0.6030150753768845 2 0.8144215117767649 1.548176659355634 1.9024999999999999
209 0.7035175879396985 2 0.833155739471515 1.5837599671081413 1.9024999999999999
210 0.8040201005025126 2 0.8523133532805002 1.6201460719660319 1.9024999999999999
211 0.9045226130653267 2 0.8719035818970928 1.6573524092790932 1.9024999999999999
212 1.0050251256281408 2 0.8919358401834159 1.6953967631587852 1.9024999999999999
213 1.105527638190955 2 0.9124197322754997 1.7342972721530518 1.9024999999999999
214 1.206030150753769 2 0.9333650547120468 1.7740724349565733 1.9024999999999999
215 1.306532663316583 2 0.9547817995856601 1.8147411161539342 1.9024999999999999
216 1.407035175879397 2 0.9766801577153058 1.8563225519930004 1.9024999999999999
217 1.5075376884422111 2 0.9990705218387202 1.8988363561856798 1.9024999999999999
218 1.6080402010050252 2 1.0219634898233763 1.9423025257330402 1.9024999999999999
219 1.7085427135678393 2 1.0453698678945689 1.9867414467716522 1.9024999999999999
220 1.8090452261306533 2 1.0693006738790738 2.0321739004377988 1.9024999999999999
221 1.9095477386934674 2 1.09376714046277 2.078621068746072 1.9024999999999999
222 2.0100502512562817 2 1.1187807184605227 2.1261045404786802 1.9024999999999999
223 2.1105527638190957 2 1.1443530800965382 2.17464631708161 1.9024999999999999
224 2.21105527638191 2 1.1704961222933141 2.224268818563624 1.9024999999999999
225 2.311557788944724 2 1.1972219699672195 2.2749948893938687 1.9024999999999999
226 2.412060301507538 2 1.2245429793286453 2.3268478043936964 1.9024999999999999
227 2.512562814070352 2 1.2524717411845692 2.379851274618095 1.9024999999999999
228 2.613065326633166 2 1.2810210842412881 2.4340294532219406 1.9024999999999999
229 2.71356783919598 2 1.310204078404978 2.4894069413060906 1.9024999999999999
230 2.814070351758794 2 1.3400340380776217 2.5460087937381055 1.9024999999999999
231 2.9145728643216082 2 1.370524525445778 2.603860524942222 1.9024999999999999
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simulation/results/data/mi_snrs.csv
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112 1.105527638190954853e+01
113 1.115577889447236259e+01
114 1.125628140703517666e+01
115 1.135678391959799072e+01
116 1.145728643216080478e+01
117 1.155778894472361884e+01
118 1.165829145728643290e+01
119 1.175879396984924696e+01
120 1.185929648241206102e+01
121 1.195979899497487509e+01
122 1.206030150753768915e+01
123 1.216080402010050321e+01
124 1.226130653266331727e+01
125 1.236180904522613133e+01
126 1.246231155778894539e+01
127 1.256281407035175945e+01
128 1.266331658291457352e+01
129 1.276381909547738758e+01
130 1.286432160804020164e+01
131 1.296482412060301570e+01
132 1.306532663316582976e+01
133 1.316582914572864382e+01
134 1.326633165829145788e+01
135 1.336683417085427195e+01
136 1.346733668341708601e+01
137 1.356783919597990007e+01
138 1.366834170854271413e+01
139 1.376884422110552819e+01
140 1.386934673366834225e+01
141 1.396984924623115631e+01
142 1.407035175879397038e+01
143 1.417085427135678444e+01
144 1.427135678391959850e+01
145 1.437185929648241256e+01
146 1.447236180904522662e+01
147 1.457286432160804068e+01
148 1.467336683417085474e+01
149 1.477386934673366881e+01
150 1.487437185929648287e+01
151 1.497487437185929693e+01
152 1.507537688442211099e+01
153 1.517587939698492505e+01
154 1.527638190954773911e+01
155 1.537688442211055317e+01
156 1.547738693467336724e+01
157 1.557788944723618130e+01
158 1.567839195979899536e+01
159 1.577889447236180942e+01
160 1.587939698492462348e+01
161 1.597989949748743754e+01
162 1.608040201005025338e+01
163 1.618090452261306567e+01
164 1.628140703517588150e+01
165 1.638190954773869379e+01
166 1.648241206030150963e+01
167 1.658291457286432191e+01
168 1.668341708542713775e+01
169 1.678391959798995003e+01
170 1.688442211055276587e+01
171 1.698492462311557816e+01
172 1.708542713567839400e+01
173 1.718592964824120628e+01
174 1.728643216080402212e+01
175 1.738693467336683440e+01
176 1.748743718592965024e+01
177 1.758793969849246253e+01
178 1.768844221105527836e+01
179 1.778894472361809065e+01
180 1.788944723618090649e+01
181 1.798994974874371877e+01
182 1.809045226130653461e+01
183 1.819095477386934689e+01
184 1.829145728643216273e+01
185 1.839195979899497502e+01
186 1.849246231155779085e+01
187 1.859296482412060314e+01
188 1.869346733668341898e+01
189 1.879396984924623126e+01
190 1.889447236180904710e+01
191 1.899497487437185939e+01
192 1.909547738693467522e+01
193 1.919597989949748751e+01
194 1.929648241206030335e+01
195 1.939698492462311563e+01
196 1.949748743718593147e+01
197 1.959798994974874375e+01
198 1.969849246231155959e+01
199 1.979899497487437188e+01
200 1.989949748743718771e+01
201 2.000000000000000000e+01
simulation/results/data/ser_per_user_mix.csv
Executable
+133
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@@ -0,0 +1,133 @@
snr_db,user,method,ser
0.0,0,OFDMA,0.8103125
0.0,0,NOMA-SIC,0.4088125
0.0,0,MAML+Attn,0.7336875
0.0,1,OFDMA,0.87896875
0.0,1,NOMA-SIC,0.4711875
0.0,1,MAML+Attn,0.76996875
0.0,2,OFDMA,0.866125
0.0,2,NOMA-SIC,0.869625
0.0,2,MAML+Attn,0.87003125
0.0,3,OFDMA,0.8666875
0.0,3,NOMA-SIC,0.967125
0.0,3,MAML+Attn,0.8644375
2.0,0,OFDMA,0.72253125
2.0,0,NOMA-SIC,0.2948125
2.0,0,MAML+Attn,0.63184375
2.0,1,OFDMA,0.8150625
2.0,1,NOMA-SIC,0.350125
2.0,1,MAML+Attn,0.66725
2.0,2,OFDMA,0.80271875
2.0,2,NOMA-SIC,0.81784375
2.0,2,MAML+Attn,0.79696875
2.0,3,OFDMA,0.80046875
2.0,3,NOMA-SIC,0.95565625
2.0,3,MAML+Attn,0.8015
4.0,0,OFDMA,0.627625
4.0,0,NOMA-SIC,0.22225
4.0,0,MAML+Attn,0.515875
4.0,1,OFDMA,0.74440625
4.0,1,NOMA-SIC,0.2644375
4.0,1,MAML+Attn,0.5648125
4.0,2,OFDMA,0.72515625
4.0,2,NOMA-SIC,0.76375
4.0,2,MAML+Attn,0.72684375
4.0,3,OFDMA,0.72503125
4.0,3,NOMA-SIC,0.94990625
4.0,3,MAML+Attn,0.7248125
6.0,0,OFDMA,0.53759375
6.0,0,NOMA-SIC,0.17559375
6.0,0,MAML+Attn,0.42209375
6.0,1,OFDMA,0.6634375
6.0,1,NOMA-SIC,0.20115625
6.0,1,MAML+Attn,0.45565625
6.0,2,OFDMA,0.64803125
6.0,2,NOMA-SIC,0.7230625
6.0,2,MAML+Attn,0.64984375
6.0,3,OFDMA,0.64903125
6.0,3,NOMA-SIC,0.9518125
6.0,3,MAML+Attn,0.64778125
8.0,0,OFDMA,0.45478125
8.0,0,NOMA-SIC,0.1343125
8.0,0,MAML+Attn,0.3250625
8.0,1,OFDMA,0.58865625
8.0,1,NOMA-SIC,0.16690625
8.0,1,MAML+Attn,0.3663125
8.0,2,OFDMA,0.572375
8.0,2,NOMA-SIC,0.69665625
8.0,2,MAML+Attn,0.57428125
8.0,3,OFDMA,0.57046875
8.0,3,NOMA-SIC,0.95575
8.0,3,MAML+Attn,0.572125
10.0,0,OFDMA,0.3806875
10.0,0,NOMA-SIC,0.11890625
10.0,0,MAML+Attn,0.257
10.0,1,OFDMA,0.51565625
10.0,1,NOMA-SIC,0.1406875
10.0,1,MAML+Attn,0.287875
10.0,2,OFDMA,0.5089375
10.0,2,NOMA-SIC,0.6810625
10.0,2,MAML+Attn,0.50996875
10.0,3,OFDMA,0.50896875
10.0,3,NOMA-SIC,0.9605625
10.0,3,MAML+Attn,0.50525
12.0,0,OFDMA,0.32140625
12.0,0,NOMA-SIC,0.103125
12.0,0,MAML+Attn,0.20309375
12.0,1,OFDMA,0.45815625
12.0,1,NOMA-SIC,0.12684375
12.0,1,MAML+Attn,0.22859375
12.0,2,OFDMA,0.4544375
12.0,2,NOMA-SIC,0.6693125
12.0,2,MAML+Attn,0.45796875
12.0,3,OFDMA,0.45409375
12.0,3,NOMA-SIC,0.96475
12.0,3,MAML+Attn,0.4513125
14.0,0,OFDMA,0.27515625
14.0,0,NOMA-SIC,0.0951875
14.0,0,MAML+Attn,0.1615
14.0,1,OFDMA,0.408625
14.0,1,NOMA-SIC,0.11925
14.0,1,MAML+Attn,0.18078125
14.0,2,OFDMA,0.406625
14.0,2,NOMA-SIC,0.67065625
14.0,2,MAML+Attn,0.40853125
14.0,3,OFDMA,0.4085
14.0,3,NOMA-SIC,0.9660625
14.0,3,MAML+Attn,0.4095625
16.0,0,OFDMA,0.24290625
16.0,0,NOMA-SIC,0.0891875
16.0,0,MAML+Attn,0.13046875
16.0,1,OFDMA,0.37471875
16.0,1,NOMA-SIC,0.111125
16.0,1,MAML+Attn,0.148875
16.0,2,OFDMA,0.3739375
16.0,2,NOMA-SIC,0.66309375
16.0,2,MAML+Attn,0.3725
16.0,3,OFDMA,0.3776875
16.0,3,NOMA-SIC,0.96734375
16.0,3,MAML+Attn,0.37828125
18.0,0,OFDMA,0.21734375
18.0,0,NOMA-SIC,0.08884375
18.0,0,MAML+Attn,0.1100625
18.0,1,OFDMA,0.3430625
18.0,1,NOMA-SIC,0.10721875
18.0,1,MAML+Attn,0.126875
18.0,2,OFDMA,0.349875
18.0,2,NOMA-SIC,0.66815625
18.0,2,MAML+Attn,0.3491875
18.0,3,OFDMA,0.3474375
18.0,3,NOMA-SIC,0.968875
18.0,3,MAML+Attn,0.34759375
20.0,0,OFDMA,0.193875
20.0,0,NOMA-SIC,0.0825625
20.0,0,MAML+Attn,0.09253125
20.0,1,OFDMA,0.3235
20.0,1,NOMA-SIC,0.103375
20.0,1,MAML+Attn,0.1100625
20.0,2,OFDMA,0.33221875
20.0,2,NOMA-SIC,0.66578125
20.0,2,MAML+Attn,0.3291875
20.0,3,OFDMA,0.33203125
20.0,3,NOMA-SIC,0.96690625
20.0,3,MAML+Attn,0.331375
1 snr_db user method ser
2 0.0 0 OFDMA 0.8103125
3 0.0 0 NOMA-SIC 0.4088125
4 0.0 0 MAML+Attn 0.7336875
5 0.0 1 OFDMA 0.87896875
6 0.0 1 NOMA-SIC 0.4711875
7 0.0 1 MAML+Attn 0.76996875
8 0.0 2 OFDMA 0.866125
9 0.0 2 NOMA-SIC 0.869625
10 0.0 2 MAML+Attn 0.87003125
11 0.0 3 OFDMA 0.8666875
12 0.0 3 NOMA-SIC 0.967125
13 0.0 3 MAML+Attn 0.8644375
14 2.0 0 OFDMA 0.72253125
15 2.0 0 NOMA-SIC 0.2948125
16 2.0 0 MAML+Attn 0.63184375
17 2.0 1 OFDMA 0.8150625
18 2.0 1 NOMA-SIC 0.350125
19 2.0 1 MAML+Attn 0.66725
20 2.0 2 OFDMA 0.80271875
21 2.0 2 NOMA-SIC 0.81784375
22 2.0 2 MAML+Attn 0.79696875
23 2.0 3 OFDMA 0.80046875
24 2.0 3 NOMA-SIC 0.95565625
25 2.0 3 MAML+Attn 0.8015
26 4.0 0 OFDMA 0.627625
27 4.0 0 NOMA-SIC 0.22225
28 4.0 0 MAML+Attn 0.515875
29 4.0 1 OFDMA 0.74440625
30 4.0 1 NOMA-SIC 0.2644375
31 4.0 1 MAML+Attn 0.5648125
32 4.0 2 OFDMA 0.72515625
33 4.0 2 NOMA-SIC 0.76375
34 4.0 2 MAML+Attn 0.72684375
35 4.0 3 OFDMA 0.72503125
36 4.0 3 NOMA-SIC 0.94990625
37 4.0 3 MAML+Attn 0.7248125
38 6.0 0 OFDMA 0.53759375
39 6.0 0 NOMA-SIC 0.17559375
40 6.0 0 MAML+Attn 0.42209375
41 6.0 1 OFDMA 0.6634375
42 6.0 1 NOMA-SIC 0.20115625
43 6.0 1 MAML+Attn 0.45565625
44 6.0 2 OFDMA 0.64803125
45 6.0 2 NOMA-SIC 0.7230625
46 6.0 2 MAML+Attn 0.64984375
47 6.0 3 OFDMA 0.64903125
48 6.0 3 NOMA-SIC 0.9518125
49 6.0 3 MAML+Attn 0.64778125
50 8.0 0 OFDMA 0.45478125
51 8.0 0 NOMA-SIC 0.1343125
52 8.0 0 MAML+Attn 0.3250625
53 8.0 1 OFDMA 0.58865625
54 8.0 1 NOMA-SIC 0.16690625
55 8.0 1 MAML+Attn 0.3663125
56 8.0 2 OFDMA 0.572375
57 8.0 2 NOMA-SIC 0.69665625
58 8.0 2 MAML+Attn 0.57428125
59 8.0 3 OFDMA 0.57046875
60 8.0 3 NOMA-SIC 0.95575
61 8.0 3 MAML+Attn 0.572125
62 10.0 0 OFDMA 0.3806875
63 10.0 0 NOMA-SIC 0.11890625
64 10.0 0 MAML+Attn 0.257
65 10.0 1 OFDMA 0.51565625
66 10.0 1 NOMA-SIC 0.1406875
67 10.0 1 MAML+Attn 0.287875
68 10.0 2 OFDMA 0.5089375
69 10.0 2 NOMA-SIC 0.6810625
70 10.0 2 MAML+Attn 0.50996875
71 10.0 3 OFDMA 0.50896875
72 10.0 3 NOMA-SIC 0.9605625
73 10.0 3 MAML+Attn 0.50525
74 12.0 0 OFDMA 0.32140625
75 12.0 0 NOMA-SIC 0.103125
76 12.0 0 MAML+Attn 0.20309375
77 12.0 1 OFDMA 0.45815625
78 12.0 1 NOMA-SIC 0.12684375
79 12.0 1 MAML+Attn 0.22859375
80 12.0 2 OFDMA 0.4544375
81 12.0 2 NOMA-SIC 0.6693125
82 12.0 2 MAML+Attn 0.45796875
83 12.0 3 OFDMA 0.45409375
84 12.0 3 NOMA-SIC 0.96475
85 12.0 3 MAML+Attn 0.4513125
86 14.0 0 OFDMA 0.27515625
87 14.0 0 NOMA-SIC 0.0951875
88 14.0 0 MAML+Attn 0.1615
89 14.0 1 OFDMA 0.408625
90 14.0 1 NOMA-SIC 0.11925
91 14.0 1 MAML+Attn 0.18078125
92 14.0 2 OFDMA 0.406625
93 14.0 2 NOMA-SIC 0.67065625
94 14.0 2 MAML+Attn 0.40853125
95 14.0 3 OFDMA 0.4085
96 14.0 3 NOMA-SIC 0.9660625
97 14.0 3 MAML+Attn 0.4095625
98 16.0 0 OFDMA 0.24290625
99 16.0 0 NOMA-SIC 0.0891875
100 16.0 0 MAML+Attn 0.13046875
101 16.0 1 OFDMA 0.37471875
102 16.0 1 NOMA-SIC 0.111125
103 16.0 1 MAML+Attn 0.148875
104 16.0 2 OFDMA 0.3739375
105 16.0 2 NOMA-SIC 0.66309375
106 16.0 2 MAML+Attn 0.3725
107 16.0 3 OFDMA 0.3776875
108 16.0 3 NOMA-SIC 0.96734375
109 16.0 3 MAML+Attn 0.37828125
110 18.0 0 OFDMA 0.21734375
111 18.0 0 NOMA-SIC 0.08884375
112 18.0 0 MAML+Attn 0.1100625
113 18.0 1 OFDMA 0.3430625
114 18.0 1 NOMA-SIC 0.10721875
115 18.0 1 MAML+Attn 0.126875
116 18.0 2 OFDMA 0.349875
117 18.0 2 NOMA-SIC 0.66815625
118 18.0 2 MAML+Attn 0.3491875
119 18.0 3 OFDMA 0.3474375
120 18.0 3 NOMA-SIC 0.968875
121 18.0 3 MAML+Attn 0.34759375
122 20.0 0 OFDMA 0.193875
123 20.0 0 NOMA-SIC 0.0825625
124 20.0 0 MAML+Attn 0.09253125
125 20.0 1 OFDMA 0.3235
126 20.0 1 NOMA-SIC 0.103375
127 20.0 1 MAML+Attn 0.1100625
128 20.0 2 OFDMA 0.33221875
129 20.0 2 NOMA-SIC 0.66578125
130 20.0 2 MAML+Attn 0.3291875
131 20.0 3 OFDMA 0.33203125
132 20.0 3 NOMA-SIC 0.96690625
133 20.0 3 MAML+Attn 0.331375
simulation/results/data/ser_scenarios.csv
Executable
+166
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snr_db,scenario,method,ser
0.0,HIGH,OFDMA,0.86378125
2.0,HIGH,OFDMA,0.7974453125
4.0,HIGH,OFDMA,0.7181328125
6.0,HIGH,OFDMA,0.640703125
8.0,HIGH,OFDMA,0.5665546875
10.0,HIGH,OFDMA,0.4988828125
12.0,HIGH,OFDMA,0.44328125
14.0,HIGH,OFDMA,0.3964296875
16.0,HIGH,OFDMA,0.3586875
18.0,HIGH,OFDMA,0.3371640625
20.0,HIGH,OFDMA,0.315140625
0.0,HIGH,NOMA-SIC,0.397921875
2.0,HIGH,NOMA-SIC,0.2678671875
4.0,HIGH,NOMA-SIC,0.1960859375
6.0,HIGH,NOMA-SIC,0.17140625
8.0,HIGH,NOMA-SIC,0.1716484375
10.0,HIGH,NOMA-SIC,0.1816953125
12.0,HIGH,NOMA-SIC,0.1922421875
14.0,HIGH,NOMA-SIC,0.19975
16.0,HIGH,NOMA-SIC,0.205078125
18.0,HIGH,NOMA-SIC,0.209296875
20.0,HIGH,NOMA-SIC,0.20921875
0.0,HIGH,MAML+Attn,0.6760703125
2.0,HIGH,MAML+Attn,0.559375
4.0,HIGH,MAML+Attn,0.4411796875
6.0,HIGH,MAML+Attn,0.3361640625
8.0,HIGH,MAML+Attn,0.251296875
10.0,HIGH,MAML+Attn,0.187703125
12.0,HIGH,MAML+Attn,0.138203125
14.0,HIGH,MAML+Attn,0.1015390625
16.0,HIGH,MAML+Attn,0.07515625
18.0,HIGH,MAML+Attn,0.0617734375
20.0,HIGH,MAML+Attn,0.048703125
0.0,LOW,OFDMA,0.853671875
2.0,LOW,OFDMA,0.7820234375
4.0,LOW,OFDMA,0.703234375
6.0,LOW,OFDMA,0.6207109375
8.0,LOW,OFDMA,0.5441640625
10.0,LOW,OFDMA,0.477578125
12.0,LOW,OFDMA,0.4244140625
14.0,LOW,OFDMA,0.378734375
16.0,LOW,OFDMA,0.3428125
18.0,LOW,OFDMA,0.3139453125
20.0,LOW,OFDMA,0.2991328125
0.0,LOW,NOMA-SIC,0.7713046875
2.0,LOW,NOMA-SIC,0.7140234375
4.0,LOW,NOMA-SIC,0.6673984375
6.0,LOW,NOMA-SIC,0.6385390625
8.0,LOW,NOMA-SIC,0.6194609375
10.0,LOW,NOMA-SIC,0.61075
12.0,LOW,NOMA-SIC,0.6003671875
14.0,LOW,NOMA-SIC,0.5958359375
16.0,LOW,NOMA-SIC,0.59475
18.0,LOW,NOMA-SIC,0.5917421875
20.0,LOW,NOMA-SIC,0.590890625
0.0,LOW,MAML+Attn,0.8527734375
2.0,LOW,MAML+Attn,0.781125
4.0,LOW,MAML+Attn,0.703015625
6.0,LOW,MAML+Attn,0.6209609375
8.0,LOW,MAML+Attn,0.543703125
10.0,LOW,MAML+Attn,0.4761796875
12.0,LOW,MAML+Attn,0.422734375
14.0,LOW,MAML+Attn,0.3783046875
16.0,LOW,MAML+Attn,0.342609375
18.0,LOW,MAML+Attn,0.314765625
20.0,LOW,MAML+Attn,0.29828125
0.0,MIX,OFDMA,0.8555234375
2.0,MIX,OFDMA,0.7851953125
4.0,MIX,OFDMA,0.7055546875
6.0,MIX,OFDMA,0.6245234375
8.0,MIX,OFDMA,0.5465703125
10.0,MIX,OFDMA,0.4785625
12.0,MIX,OFDMA,0.4220234375
14.0,MIX,OFDMA,0.3747265625
16.0,MIX,OFDMA,0.3423125
18.0,MIX,OFDMA,0.3144296875
20.0,MIX,OFDMA,0.29540625
0.0,MIX,NOMA-SIC,0.6791875
2.0,MIX,NOMA-SIC,0.604609375
4.0,MIX,NOMA-SIC,0.5500859375
6.0,MIX,NOMA-SIC,0.51290625
8.0,MIX,NOMA-SIC,0.48840625
10.0,MIX,NOMA-SIC,0.4753046875
12.0,MIX,NOMA-SIC,0.4660078125
14.0,MIX,NOMA-SIC,0.4627890625
16.0,MIX,NOMA-SIC,0.4576875
18.0,MIX,NOMA-SIC,0.4582734375
20.0,MIX,NOMA-SIC,0.45465625
0.0,MIX,MAML+Attn,0.80953125
2.0,MIX,MAML+Attn,0.724390625
4.0,MIX,MAML+Attn,0.6330859375
6.0,MIX,MAML+Attn,0.54384375
8.0,MIX,MAML+Attn,0.4594453125
10.0,MIX,MAML+Attn,0.3900234375
12.0,MIX,MAML+Attn,0.3352421875
14.0,MIX,MAML+Attn,0.29009375
16.0,MIX,MAML+Attn,0.25753125
18.0,MIX,MAML+Attn,0.2334296875
20.0,MIX,MAML+Attn,0.2157890625
0.0,HETERO,OFDMA,0.860828125
2.0,HETERO,OFDMA,0.790703125
4.0,HETERO,OFDMA,0.7110625
6.0,HETERO,OFDMA,0.6295625
8.0,HETERO,OFDMA,0.551546875
10.0,HETERO,OFDMA,0.4834296875
12.0,HETERO,OFDMA,0.4229609375
14.0,HETERO,OFDMA,0.3796015625
16.0,HETERO,OFDMA,0.343703125
18.0,HETERO,OFDMA,0.3148671875
20.0,HETERO,OFDMA,0.297796875
0.0,HETERO,NOMA-SIC,0.527703125
2.0,HETERO,NOMA-SIC,0.431765625
4.0,HETERO,NOMA-SIC,0.3684765625
6.0,HETERO,NOMA-SIC,0.3273984375
8.0,HETERO,NOMA-SIC,0.3060625
10.0,HETERO,NOMA-SIC,0.2964765625
12.0,HETERO,NOMA-SIC,0.293234375
14.0,HETERO,NOMA-SIC,0.2929921875
16.0,HETERO,NOMA-SIC,0.2934609375
18.0,HETERO,NOMA-SIC,0.295640625
20.0,HETERO,NOMA-SIC,0.2960546875
0.0,HETERO,MAML+Attn,0.7454375
2.0,HETERO,MAML+Attn,0.6441953125
4.0,HETERO,MAML+Attn,0.5402265625
6.0,HETERO,MAML+Attn,0.4423828125
8.0,HETERO,MAML+Attn,0.359296875
10.0,HETERO,MAML+Attn,0.290953125
12.0,HETERO,MAML+Attn,0.2393046875
14.0,HETERO,MAML+Attn,0.2000703125
16.0,HETERO,MAML+Attn,0.170671875
18.0,HETERO,MAML+Attn,0.15109375
20.0,HETERO,MAML+Attn,0.1367734375
0.0,ASYM,OFDMA,0.85534375
2.0,ASYM,OFDMA,0.786015625
4.0,ASYM,OFDMA,0.7062265625
6.0,ASYM,OFDMA,0.6269609375
8.0,ASYM,OFDMA,0.548
10.0,ASYM,OFDMA,0.482671875
12.0,ASYM,OFDMA,0.426046875
14.0,ASYM,OFDMA,0.379328125
16.0,ASYM,OFDMA,0.3443046875
18.0,ASYM,OFDMA,0.31640625
20.0,ASYM,OFDMA,0.297921875
0.0,ASYM,NOMA-SIC,0.5972578125
2.0,ASYM,NOMA-SIC,0.50465625
4.0,ASYM,NOMA-SIC,0.431984375
6.0,ASYM,NOMA-SIC,0.388
8.0,ASYM,NOMA-SIC,0.364328125
10.0,ASYM,NOMA-SIC,0.353625
12.0,ASYM,NOMA-SIC,0.3493828125
14.0,ASYM,NOMA-SIC,0.346359375
16.0,ASYM,NOMA-SIC,0.345421875
18.0,ASYM,NOMA-SIC,0.34753125
20.0,ASYM,NOMA-SIC,0.3463828125
0.0,ASYM,MAML+Attn,0.7932421875
2.0,ASYM,MAML+Attn,0.7028828125
4.0,ASYM,MAML+Attn,0.6067109375
6.0,ASYM,MAML+Attn,0.5111484375
8.0,ASYM,MAML+Attn,0.428078125
10.0,ASYM,MAML+Attn,0.3584140625
12.0,ASYM,MAML+Attn,0.302203125
14.0,ASYM,MAML+Attn,0.2571328125
16.0,ASYM,MAML+Attn,0.2235234375
18.0,ASYM,MAML+Attn,0.1992734375
20.0,ASYM,MAML+Attn,0.1825859375
1 snr_db scenario method ser
2 0.0 HIGH OFDMA 0.86378125
3 2.0 HIGH OFDMA 0.7974453125
4 4.0 HIGH OFDMA 0.7181328125
5 6.0 HIGH OFDMA 0.640703125
6 8.0 HIGH OFDMA 0.5665546875
7 10.0 HIGH OFDMA 0.4988828125
8 12.0 HIGH OFDMA 0.44328125
9 14.0 HIGH OFDMA 0.3964296875
10 16.0 HIGH OFDMA 0.3586875
11 18.0 HIGH OFDMA 0.3371640625
12 20.0 HIGH OFDMA 0.315140625
13 0.0 HIGH NOMA-SIC 0.397921875
14 2.0 HIGH NOMA-SIC 0.2678671875
15 4.0 HIGH NOMA-SIC 0.1960859375
16 6.0 HIGH NOMA-SIC 0.17140625
17 8.0 HIGH NOMA-SIC 0.1716484375
18 10.0 HIGH NOMA-SIC 0.1816953125
19 12.0 HIGH NOMA-SIC 0.1922421875
20 14.0 HIGH NOMA-SIC 0.19975
21 16.0 HIGH NOMA-SIC 0.205078125
22 18.0 HIGH NOMA-SIC 0.209296875
23 20.0 HIGH NOMA-SIC 0.20921875
24 0.0 HIGH MAML+Attn 0.6760703125
25 2.0 HIGH MAML+Attn 0.559375
26 4.0 HIGH MAML+Attn 0.4411796875
27 6.0 HIGH MAML+Attn 0.3361640625
28 8.0 HIGH MAML+Attn 0.251296875
29 10.0 HIGH MAML+Attn 0.187703125
30 12.0 HIGH MAML+Attn 0.138203125
31 14.0 HIGH MAML+Attn 0.1015390625
32 16.0 HIGH MAML+Attn 0.07515625
33 18.0 HIGH MAML+Attn 0.0617734375
34 20.0 HIGH MAML+Attn 0.048703125
35 0.0 LOW OFDMA 0.853671875
36 2.0 LOW OFDMA 0.7820234375
37 4.0 LOW OFDMA 0.703234375
38 6.0 LOW OFDMA 0.6207109375
39 8.0 LOW OFDMA 0.5441640625
40 10.0 LOW OFDMA 0.477578125
41 12.0 LOW OFDMA 0.4244140625
42 14.0 LOW OFDMA 0.378734375
43 16.0 LOW OFDMA 0.3428125
44 18.0 LOW OFDMA 0.3139453125
45 20.0 LOW OFDMA 0.2991328125
46 0.0 LOW NOMA-SIC 0.7713046875
47 2.0 LOW NOMA-SIC 0.7140234375
48 4.0 LOW NOMA-SIC 0.6673984375
49 6.0 LOW NOMA-SIC 0.6385390625
50 8.0 LOW NOMA-SIC 0.6194609375
51 10.0 LOW NOMA-SIC 0.61075
52 12.0 LOW NOMA-SIC 0.6003671875
53 14.0 LOW NOMA-SIC 0.5958359375
54 16.0 LOW NOMA-SIC 0.59475
55 18.0 LOW NOMA-SIC 0.5917421875
56 20.0 LOW NOMA-SIC 0.590890625
57 0.0 LOW MAML+Attn 0.8527734375
58 2.0 LOW MAML+Attn 0.781125
59 4.0 LOW MAML+Attn 0.703015625
60 6.0 LOW MAML+Attn 0.6209609375
61 8.0 LOW MAML+Attn 0.543703125
62 10.0 LOW MAML+Attn 0.4761796875
63 12.0 LOW MAML+Attn 0.422734375
64 14.0 LOW MAML+Attn 0.3783046875
65 16.0 LOW MAML+Attn 0.342609375
66 18.0 LOW MAML+Attn 0.314765625
67 20.0 LOW MAML+Attn 0.29828125
68 0.0 MIX OFDMA 0.8555234375
69 2.0 MIX OFDMA 0.7851953125
70 4.0 MIX OFDMA 0.7055546875
71 6.0 MIX OFDMA 0.6245234375
72 8.0 MIX OFDMA 0.5465703125
73 10.0 MIX OFDMA 0.4785625
74 12.0 MIX OFDMA 0.4220234375
75 14.0 MIX OFDMA 0.3747265625
76 16.0 MIX OFDMA 0.3423125
77 18.0 MIX OFDMA 0.3144296875
78 20.0 MIX OFDMA 0.29540625
79 0.0 MIX NOMA-SIC 0.6791875
80 2.0 MIX NOMA-SIC 0.604609375
81 4.0 MIX NOMA-SIC 0.5500859375
82 6.0 MIX NOMA-SIC 0.51290625
83 8.0 MIX NOMA-SIC 0.48840625
84 10.0 MIX NOMA-SIC 0.4753046875
85 12.0 MIX NOMA-SIC 0.4660078125
86 14.0 MIX NOMA-SIC 0.4627890625
87 16.0 MIX NOMA-SIC 0.4576875
88 18.0 MIX NOMA-SIC 0.4582734375
89 20.0 MIX NOMA-SIC 0.45465625
90 0.0 MIX MAML+Attn 0.80953125
91 2.0 MIX MAML+Attn 0.724390625
92 4.0 MIX MAML+Attn 0.6330859375
93 6.0 MIX MAML+Attn 0.54384375
94 8.0 MIX MAML+Attn 0.4594453125
95 10.0 MIX MAML+Attn 0.3900234375
96 12.0 MIX MAML+Attn 0.3352421875
97 14.0 MIX MAML+Attn 0.29009375
98 16.0 MIX MAML+Attn 0.25753125
99 18.0 MIX MAML+Attn 0.2334296875
100 20.0 MIX MAML+Attn 0.2157890625
101 0.0 HETERO OFDMA 0.860828125
102 2.0 HETERO OFDMA 0.790703125
103 4.0 HETERO OFDMA 0.7110625
104 6.0 HETERO OFDMA 0.6295625
105 8.0 HETERO OFDMA 0.551546875
106 10.0 HETERO OFDMA 0.4834296875
107 12.0 HETERO OFDMA 0.4229609375
108 14.0 HETERO OFDMA 0.3796015625
109 16.0 HETERO OFDMA 0.343703125
110 18.0 HETERO OFDMA 0.3148671875
111 20.0 HETERO OFDMA 0.297796875
112 0.0 HETERO NOMA-SIC 0.527703125
113 2.0 HETERO NOMA-SIC 0.431765625
114 4.0 HETERO NOMA-SIC 0.3684765625
115 6.0 HETERO NOMA-SIC 0.3273984375
116 8.0 HETERO NOMA-SIC 0.3060625
117 10.0 HETERO NOMA-SIC 0.2964765625
118 12.0 HETERO NOMA-SIC 0.293234375
119 14.0 HETERO NOMA-SIC 0.2929921875
120 16.0 HETERO NOMA-SIC 0.2934609375
121 18.0 HETERO NOMA-SIC 0.295640625
122 20.0 HETERO NOMA-SIC 0.2960546875
123 0.0 HETERO MAML+Attn 0.7454375
124 2.0 HETERO MAML+Attn 0.6441953125
125 4.0 HETERO MAML+Attn 0.5402265625
126 6.0 HETERO MAML+Attn 0.4423828125
127 8.0 HETERO MAML+Attn 0.359296875
128 10.0 HETERO MAML+Attn 0.290953125
129 12.0 HETERO MAML+Attn 0.2393046875
130 14.0 HETERO MAML+Attn 0.2000703125
131 16.0 HETERO MAML+Attn 0.170671875
132 18.0 HETERO MAML+Attn 0.15109375
133 20.0 HETERO MAML+Attn 0.1367734375
134 0.0 ASYM OFDMA 0.85534375
135 2.0 ASYM OFDMA 0.786015625
136 4.0 ASYM OFDMA 0.7062265625
137 6.0 ASYM OFDMA 0.6269609375
138 8.0 ASYM OFDMA 0.548
139 10.0 ASYM OFDMA 0.482671875
140 12.0 ASYM OFDMA 0.426046875
141 14.0 ASYM OFDMA 0.379328125
142 16.0 ASYM OFDMA 0.3443046875
143 18.0 ASYM OFDMA 0.31640625
144 20.0 ASYM OFDMA 0.297921875
145 0.0 ASYM NOMA-SIC 0.5972578125
146 2.0 ASYM NOMA-SIC 0.50465625
147 4.0 ASYM NOMA-SIC 0.431984375
148 6.0 ASYM NOMA-SIC 0.388
149 8.0 ASYM NOMA-SIC 0.364328125
150 10.0 ASYM NOMA-SIC 0.353625
151 12.0 ASYM NOMA-SIC 0.3493828125
152 14.0 ASYM NOMA-SIC 0.346359375
153 16.0 ASYM NOMA-SIC 0.345421875
154 18.0 ASYM NOMA-SIC 0.34753125
155 20.0 ASYM NOMA-SIC 0.3463828125
156 0.0 ASYM MAML+Attn 0.7932421875
157 2.0 ASYM MAML+Attn 0.7028828125
158 4.0 ASYM MAML+Attn 0.6067109375
159 6.0 ASYM MAML+Attn 0.5111484375
160 8.0 ASYM MAML+Attn 0.428078125
161 10.0 ASYM MAML+Attn 0.3584140625
162 12.0 ASYM MAML+Attn 0.302203125
163 14.0 ASYM MAML+Attn 0.2571328125
164 16.0 ASYM MAML+Attn 0.2235234375
165 18.0 ASYM MAML+Attn 0.1992734375
166 20.0 ASYM MAML+Attn 0.1825859375
simulation/results/data/snr_db.csv
Executable
+12
View File
@@ -0,0 +1,12 @@
snr_db
0.000000000000000000e+00
2.000000000000000000e+00
4.000000000000000000e+00
6.000000000000000000e+00
8.000000000000000000e+00
1.000000000000000000e+01
1.200000000000000000e+01
1.400000000000000000e+01
1.600000000000000000e+01
1.800000000000000000e+01
2.000000000000000000e+01
1 snr_db
2 0.000000000000000000e+00
3 2.000000000000000000e+00
4 4.000000000000000000e+00
5 6.000000000000000000e+00
6 8.000000000000000000e+00
7 1.000000000000000000e+01
8 1.200000000000000000e+01
9 1.400000000000000000e+01
10 1.600000000000000000e+01
11 1.800000000000000000e+01
12 2.000000000000000000e+01
simulation/results/data/snr_f12.csv
Executable
+22
View File
@@ -0,0 +1,22 @@
snr_db
0.000000000000000000e+00
1.000000000000000000e+00
2.000000000000000000e+00
3.000000000000000000e+00
4.000000000000000000e+00
5.000000000000000000e+00
6.000000000000000000e+00
7.000000000000000000e+00
8.000000000000000000e+00
9.000000000000000000e+00
1.000000000000000000e+01
1.100000000000000000e+01
1.200000000000000000e+01
1.300000000000000000e+01
1.400000000000000000e+01
1.500000000000000000e+01
1.600000000000000000e+01
1.700000000000000000e+01
1.800000000000000000e+01
1.900000000000000000e+01
2.000000000000000000e+01
1 snr_db
2 0.000000000000000000e+00
3 1.000000000000000000e+00
4 2.000000000000000000e+00
5 3.000000000000000000e+00
6 4.000000000000000000e+00
7 5.000000000000000000e+00
8 6.000000000000000000e+00
9 7.000000000000000000e+00
10 8.000000000000000000e+00
11 9.000000000000000000e+00
12 1.000000000000000000e+01
13 1.100000000000000000e+01
14 1.200000000000000000e+01
15 1.300000000000000000e+01
16 1.400000000000000000e+01
17 1.500000000000000000e+01
18 1.600000000000000000e+01
19 1.700000000000000000e+01
20 1.800000000000000000e+01
21 1.900000000000000000e+01
22 2.000000000000000000e+01
simulation/results/data/u_variation_f12.csv
Executable
+505
View File
@@ -0,0 +1,505 @@
snr_db,beta,U,method,ser
0.0,0.9,1,OFDMA,0.2277109375
0.0,0.9,1,UWCA,0.2277109375
1.0,0.9,1,OFDMA,0.186171875
1.0,0.9,1,UWCA,0.186171875
2.0,0.9,1,OFDMA,0.15054427083333333
2.0,0.9,1,UWCA,0.15054427083333333
3.0,0.9,1,OFDMA,0.12235416666666667
3.0,0.9,1,UWCA,0.12235416666666667
4.0,0.9,1,OFDMA,0.09946875
4.0,0.9,1,UWCA,0.09946875
5.0,0.9,1,OFDMA,0.0797890625
5.0,0.9,1,UWCA,0.0797890625
6.0,0.9,1,OFDMA,0.06358072916666667
6.0,0.9,1,UWCA,0.06358072916666667
7.0,0.9,1,OFDMA,0.05181510416666667
7.0,0.9,1,UWCA,0.05181510416666667
8.0,0.9,1,OFDMA,0.04116145833333333
8.0,0.9,1,UWCA,0.04116145833333333
9.0,0.9,1,OFDMA,0.032716145833333335
9.0,0.9,1,UWCA,0.032716145833333335
10.0,0.9,1,OFDMA,0.026020833333333333
10.0,0.9,1,UWCA,0.026020833333333333
11.0,0.9,1,OFDMA,0.021356770833333334
11.0,0.9,1,UWCA,0.021356770833333334
12.0,0.9,1,OFDMA,0.015979166666666666
12.0,0.9,1,UWCA,0.015979166666666666
13.0,0.9,1,OFDMA,0.013575520833333334
13.0,0.9,1,UWCA,0.013575520833333334
14.0,0.9,1,OFDMA,0.010421875
14.0,0.9,1,UWCA,0.010421875
15.0,0.9,1,OFDMA,0.008375
15.0,0.9,1,UWCA,0.008375
16.0,0.9,1,OFDMA,0.006455729166666667
16.0,0.9,1,UWCA,0.006455729166666667
17.0,0.9,1,OFDMA,0.005231770833333333
17.0,0.9,1,UWCA,0.005231770833333333
18.0,0.9,1,OFDMA,0.004354166666666667
18.0,0.9,1,UWCA,0.004354166666666667
19.0,0.9,1,OFDMA,0.003380208333333333
19.0,0.9,1,UWCA,0.003380208333333333
20.0,0.9,1,OFDMA,0.0026927083333333334
20.0,0.9,1,UWCA,0.0026927083333333334
0.0,0.9,2,OFDMA,0.48298307291666664
0.0,0.9,2,UWCA,0.27999869791666665
1.0,0.9,2,OFDMA,0.41434765625
1.0,0.9,2,UWCA,0.23050651041666667
2.0,0.9,2,OFDMA,0.35125260416666665
2.0,0.9,2,UWCA,0.1897265625
3.0,0.9,2,OFDMA,0.29434635416666666
3.0,0.9,2,UWCA,0.1546640625
4.0,0.9,2,OFDMA,0.24390104166666668
4.0,0.9,2,UWCA,0.1256484375
5.0,0.9,2,OFDMA,0.20093880208333334
5.0,0.9,2,UWCA,0.10131510416666667
6.0,0.9,2,OFDMA,0.16413411458333332
6.0,0.9,2,UWCA,0.08115234375
7.0,0.9,2,OFDMA,0.13369270833333333
7.0,0.9,2,UWCA,0.06594140625
8.0,0.9,2,OFDMA,0.10834375
8.0,0.9,2,UWCA,0.05243619791666667
9.0,0.9,2,OFDMA,0.08767447916666667
9.0,0.9,2,UWCA,0.04204817708333333
10.0,0.9,2,OFDMA,0.06969791666666666
10.0,0.9,2,UWCA,0.033365885416666664
11.0,0.9,2,OFDMA,0.05624609375
11.0,0.9,2,UWCA,0.026709635416666665
12.0,0.9,2,OFDMA,0.045184895833333336
12.0,0.9,2,UWCA,0.021680989583333334
13.0,0.9,2,OFDMA,0.03581119791666667
13.0,0.9,2,UWCA,0.016889322916666668
14.0,0.9,2,OFDMA,0.029057291666666665
14.0,0.9,2,UWCA,0.013725260416666666
15.0,0.9,2,OFDMA,0.02318359375
15.0,0.9,2,UWCA,0.010756510416666667
16.0,0.9,2,OFDMA,0.01820703125
16.0,0.9,2,UWCA,0.008615885416666667
17.0,0.9,2,OFDMA,0.014565104166666667
17.0,0.9,2,UWCA,0.0068671875
18.0,0.9,2,OFDMA,0.011600260416666666
18.0,0.9,2,UWCA,0.00551953125
19.0,0.9,2,OFDMA,0.009049479166666667
19.0,0.9,2,UWCA,0.004247395833333333
20.0,0.9,2,OFDMA,0.007338541666666667
20.0,0.9,2,UWCA,0.003494791666666667
0.0,0.9,3,OFDMA,0.7357656250000003
0.0,0.9,3,UWCA,0.30170746527777786
1.0,0.9,3,OFDMA,0.6863949652777775
1.0,0.9,3,UWCA,0.2504392361111111
2.0,0.9,3,OFDMA,0.6347604166666653
2.0,0.9,3,UWCA,0.20678732638888933
3.0,0.9,3,OFDMA,0.5824887152777778
3.0,0.9,3,UWCA,0.16832638888888882
4.0,0.9,3,OFDMA,0.5298906250000003
4.0,0.9,3,UWCA,0.1375590277777777
5.0,0.9,3,OFDMA,0.47926996527777777
5.0,0.9,3,UWCA,0.11143576388888887
6.0,0.9,3,OFDMA,0.4304539930555558
6.0,0.9,3,UWCA,0.08954427083333347
7.0,0.9,3,OFDMA,0.38406684027777804
7.0,0.9,3,UWCA,0.07243142361111098
8.0,0.9,3,OFDMA,0.34143576388888847
8.0,0.9,3,UWCA,0.05816579861111104
9.0,0.9,3,OFDMA,0.30193142361111125
9.0,0.9,3,UWCA,0.04592013888888885
10.0,0.9,3,OFDMA,0.26722135416666665
10.0,0.9,3,UWCA,0.037015624999999976
11.0,0.9,3,OFDMA,0.23677170138888876
11.0,0.9,3,UWCA,0.02993315972222221
12.0,0.9,3,OFDMA,0.20839843749999995
12.0,0.9,3,UWCA,0.023803819444444475
13.0,0.9,3,OFDMA,0.1833237847222221
13.0,0.9,3,UWCA,0.018681423611111116
14.0,0.9,3,OFDMA,0.1632864583333333
14.0,0.9,3,UWCA,0.015031250000000036
15.0,0.9,3,OFDMA,0.1451927083333333
15.0,0.9,3,UWCA,0.012011284722222248
16.0,0.9,3,OFDMA,0.13011024305555544
16.0,0.9,3,UWCA,0.009515624999999995
17.0,0.9,3,OFDMA,0.11676909722222234
17.0,0.9,3,UWCA,0.007711805555555572
18.0,0.9,3,OFDMA,0.10585677083333331
18.0,0.9,3,UWCA,0.006151041666666654
19.0,0.9,3,OFDMA,0.09616927083333313
19.0,0.9,3,UWCA,0.00486892361111111
20.0,0.9,3,OFDMA,0.08875954861111118
20.0,0.9,3,UWCA,0.003828125000000005
0.0,0.9,4,OFDMA,0.8781588541666666
0.0,0.9,4,UWCA,0.315431640625
1.0,0.9,4,OFDMA,0.843181640625
1.0,0.9,4,UWCA,0.262328125
2.0,0.9,4,OFDMA,0.804537109375
2.0,0.9,4,UWCA,0.21639127604166666
3.0,0.9,4,OFDMA,0.76352734375
3.0,0.9,4,UWCA,0.177685546875
4.0,0.9,4,OFDMA,0.7189934895833333
4.0,0.9,4,UWCA,0.14457486979166667
5.0,0.9,4,OFDMA,0.6737454427083334
5.0,0.9,4,UWCA,0.11712369791666667
6.0,0.9,4,OFDMA,0.6293020833333334
6.0,0.9,4,UWCA,0.09453580729166666
7.0,0.9,4,OFDMA,0.5850514322916667
7.0,0.9,4,UWCA,0.07621223958333333
8.0,0.9,4,OFDMA,0.5429192708333334
8.0,0.9,4,UWCA,0.060830078125
9.0,0.9,4,OFDMA,0.50422265625
9.0,0.9,4,UWCA,0.04935611979166667
10.0,0.9,4,OFDMA,0.46758268229166666
10.0,0.9,4,UWCA,0.039248046875
11.0,0.9,4,OFDMA,0.43517578125
11.0,0.9,4,UWCA,0.031373046875
12.0,0.9,4,OFDMA,0.4051640625
12.0,0.9,4,UWCA,0.024932291666666665
13.0,0.9,4,OFDMA,0.3789290364583333
13.0,0.9,4,UWCA,0.019959635416666666
14.0,0.9,4,OFDMA,0.3555846354166667
14.0,0.9,4,UWCA,0.01582421875
15.0,0.9,4,OFDMA,0.335716796875
15.0,0.9,4,UWCA,0.012673828125
16.0,0.9,4,OFDMA,0.31779752604166667
16.0,0.9,4,UWCA,0.010057942708333333
17.0,0.9,4,OFDMA,0.302654296875
17.0,0.9,4,UWCA,0.00795703125
18.0,0.9,4,OFDMA,0.2898072916666667
18.0,0.9,4,UWCA,0.006319010416666666
19.0,0.9,4,OFDMA,0.27835221354166667
19.0,0.9,4,UWCA,0.00501171875
20.0,0.9,4,OFDMA,0.269482421875
20.0,0.9,4,UWCA,0.003940755208333333
0.0,0.5,1,OFDMA,0.22677604166666668
0.0,0.5,1,UWCA,0.22677604166666668
1.0,0.5,1,OFDMA,0.18487239583333334
1.0,0.5,1,UWCA,0.18487239583333334
2.0,0.5,1,OFDMA,0.15238541666666666
2.0,0.5,1,UWCA,0.15238541666666666
3.0,0.5,1,OFDMA,0.1217421875
3.0,0.5,1,UWCA,0.1217421875
4.0,0.5,1,OFDMA,0.09941145833333333
4.0,0.5,1,UWCA,0.09941145833333333
5.0,0.5,1,OFDMA,0.07958333333333334
5.0,0.5,1,UWCA,0.07958333333333334
6.0,0.5,1,OFDMA,0.06379947916666667
6.0,0.5,1,UWCA,0.06379947916666667
7.0,0.5,1,OFDMA,0.051625
7.0,0.5,1,UWCA,0.051625
8.0,0.5,1,OFDMA,0.04043229166666667
8.0,0.5,1,UWCA,0.04043229166666667
9.0,0.5,1,OFDMA,0.0323203125
9.0,0.5,1,UWCA,0.0323203125
10.0,0.5,1,OFDMA,0.025927083333333333
10.0,0.5,1,UWCA,0.025927083333333333
11.0,0.5,1,OFDMA,0.020786458333333334
11.0,0.5,1,UWCA,0.020786458333333334
12.0,0.5,1,OFDMA,0.016614583333333332
12.0,0.5,1,UWCA,0.016614583333333332
13.0,0.5,1,OFDMA,0.013325520833333333
13.0,0.5,1,UWCA,0.013325520833333333
14.0,0.5,1,OFDMA,0.010674479166666667
14.0,0.5,1,UWCA,0.010674479166666667
15.0,0.5,1,OFDMA,0.008361979166666667
15.0,0.5,1,UWCA,0.008361979166666667
16.0,0.5,1,OFDMA,0.006419270833333333
16.0,0.5,1,UWCA,0.006419270833333333
17.0,0.5,1,OFDMA,0.005421875
17.0,0.5,1,UWCA,0.005421875
18.0,0.5,1,OFDMA,0.004419270833333333
18.0,0.5,1,UWCA,0.004419270833333333
19.0,0.5,1,OFDMA,0.0032708333333333335
19.0,0.5,1,UWCA,0.0032708333333333335
20.0,0.5,1,OFDMA,0.00253125
20.0,0.5,1,UWCA,0.00253125
0.0,0.5,2,OFDMA,0.49812630208333336
0.0,0.5,2,UWCA,0.46684375
1.0,0.5,2,OFDMA,0.43124609375
1.0,0.5,2,UWCA,0.40104557291666665
2.0,0.5,2,OFDMA,0.3684453125
2.0,0.5,2,UWCA,0.34113932291666665
3.0,0.5,2,OFDMA,0.31294921875
3.0,0.5,2,UWCA,0.28744921875
4.0,0.5,2,OFDMA,0.2618932291666667
4.0,0.5,2,UWCA,0.2390859375
5.0,0.5,2,OFDMA,0.2174921875
5.0,0.5,2,UWCA,0.19773307291666667
6.0,0.5,2,OFDMA,0.18020442708333334
6.0,0.5,2,UWCA,0.163140625
7.0,0.5,2,OFDMA,0.14807421875
7.0,0.5,2,UWCA,0.13353776041666668
8.0,0.5,2,OFDMA,0.120546875
8.0,0.5,2,UWCA,0.10822786458333333
9.0,0.5,2,OFDMA,0.09825911458333333
9.0,0.5,2,UWCA,0.08794661458333333
10.0,0.5,2,OFDMA,0.07884114583333333
10.0,0.5,2,UWCA,0.07050911458333334
11.0,0.5,2,OFDMA,0.06336979166666666
11.0,0.5,2,UWCA,0.05633463541666667
12.0,0.5,2,OFDMA,0.05137760416666667
12.0,0.5,2,UWCA,0.045634114583333336
13.0,0.5,2,OFDMA,0.040665364583333335
13.0,0.5,2,UWCA,0.03613932291666667
14.0,0.5,2,OFDMA,0.03282291666666667
14.0,0.5,2,UWCA,0.02910546875
15.0,0.5,2,OFDMA,0.026138020833333334
15.0,0.5,2,UWCA,0.023204427083333333
16.0,0.5,2,OFDMA,0.02068359375
16.0,0.5,2,UWCA,0.018377604166666665
17.0,0.5,2,OFDMA,0.0166796875
17.0,0.5,2,UWCA,0.014713541666666666
18.0,0.5,2,OFDMA,0.013592447916666667
18.0,0.5,2,UWCA,0.012067708333333333
19.0,0.5,2,OFDMA,0.010821614583333333
19.0,0.5,2,UWCA,0.009576822916666667
20.0,0.5,2,OFDMA,0.0086328125
20.0,0.5,2,UWCA,0.007565104166666667
0.0,0.5,3,OFDMA,0.7357057291666653
0.0,0.5,3,UWCA,0.6688385416666659
1.0,0.5,3,OFDMA,0.6836770833333334
1.0,0.5,3,UWCA,0.6091597222222226
2.0,0.5,3,OFDMA,0.6284079861111111
2.0,0.5,3,UWCA,0.548318576388889
3.0,0.5,3,OFDMA,0.5731371527777781
3.0,0.5,3,UWCA,0.4887960069444456
4.0,0.5,3,OFDMA,0.5188046874999994
4.0,0.5,3,UWCA,0.4321979166666668
5.0,0.5,3,OFDMA,0.4656076388888891
5.0,0.5,3,UWCA,0.3781883680555565
6.0,0.5,3,OFDMA,0.41499479166666636
6.0,0.5,3,UWCA,0.3288480902777776
7.0,0.5,3,OFDMA,0.36824218750000015
7.0,0.5,3,UWCA,0.28369010416666696
8.0,0.5,3,OFDMA,0.32604600694444463
8.0,0.5,3,UWCA,0.2442039930555557
9.0,0.5,3,OFDMA,0.28732899305555554
9.0,0.5,3,UWCA,0.20949305555555559
10.0,0.5,3,OFDMA,0.254361111111111
10.0,0.5,3,UWCA,0.17940104166666673
11.0,0.5,3,OFDMA,0.2233940972222222
11.0,0.5,3,UWCA,0.15297916666666672
12.0,0.5,3,OFDMA,0.19677864583333357
12.0,0.5,3,UWCA,0.13052864583333318
13.0,0.5,3,OFDMA,0.1747682291666668
13.0,0.5,3,UWCA,0.11215451388888888
14.0,0.5,3,OFDMA,0.15551996527777784
14.0,0.5,3,UWCA,0.09648263888888882
15.0,0.5,3,OFDMA,0.13859375000000004
15.0,0.5,3,UWCA,0.08325000000000007
16.0,0.5,3,OFDMA,0.12402083333333326
16.0,0.5,3,UWCA,0.0724184027777778
17.0,0.5,3,OFDMA,0.11267361111111107
17.0,0.5,3,UWCA,0.0631432291666666
18.0,0.5,3,OFDMA,0.10258767361111107
18.0,0.5,3,UWCA,0.05576736111111118
19.0,0.5,3,OFDMA,0.09442881944444444
19.0,0.5,3,UWCA,0.049179687499999965
20.0,0.5,3,OFDMA,0.0873802083333334
20.0,0.5,3,UWCA,0.04426736111111111
0.0,0.5,4,OFDMA,0.86390625
0.0,0.5,4,UWCA,0.7880390625
1.0,0.5,4,OFDMA,0.832029296875
1.0,0.5,4,UWCA,0.7429388020833333
2.0,0.5,4,OFDMA,0.7977337239583333
2.0,0.5,4,UWCA,0.6948938802083333
3.0,0.5,4,OFDMA,0.7601510416666667
3.0,0.5,4,UWCA,0.64496484375
4.0,0.5,4,OFDMA,0.722421875
4.0,0.5,4,UWCA,0.595236328125
5.0,0.5,4,OFDMA,0.683517578125
5.0,0.5,4,UWCA,0.5458072916666666
6.0,0.5,4,OFDMA,0.6447838541666666
6.0,0.5,4,UWCA,0.4987213541666667
7.0,0.5,4,OFDMA,0.6064576822916666
7.0,0.5,4,UWCA,0.453876953125
8.0,0.5,4,OFDMA,0.5705520833333333
8.0,0.5,4,UWCA,0.412064453125
9.0,0.5,4,OFDMA,0.536923828125
9.0,0.5,4,UWCA,0.3736608072916667
10.0,0.5,4,OFDMA,0.50454296875
10.0,0.5,4,UWCA,0.33835807291666664
11.0,0.5,4,OFDMA,0.476373046875
11.0,0.5,4,UWCA,0.30787044270833336
12.0,0.5,4,OFDMA,0.450078125
12.0,0.5,4,UWCA,0.280119140625
13.0,0.5,4,OFDMA,0.42621809895833335
13.0,0.5,4,UWCA,0.2567434895833333
14.0,0.5,4,OFDMA,0.4048450520833333
14.0,0.5,4,UWCA,0.23559049479166666
15.0,0.5,4,OFDMA,0.3863958333333333
15.0,0.5,4,UWCA,0.21690234375
16.0,0.5,4,OFDMA,0.3704524739583333
16.0,0.5,4,UWCA,0.20187565104166666
17.0,0.5,4,OFDMA,0.35684765625
17.0,0.5,4,UWCA,0.18953190104166667
18.0,0.5,4,OFDMA,0.343931640625
18.0,0.5,4,UWCA,0.17797526041666667
19.0,0.5,4,OFDMA,0.333697265625
19.0,0.5,4,UWCA,0.16815950520833334
20.0,0.5,4,OFDMA,0.32444466145833334
20.0,0.5,4,UWCA,0.16032161458333333
0.0,0.1,1,OFDMA,0.22649479166666667
0.0,0.1,1,UWCA,0.22649479166666667
1.0,0.1,1,OFDMA,0.18648958333333332
1.0,0.1,1,UWCA,0.18648958333333332
2.0,0.1,1,OFDMA,0.1515
2.0,0.1,1,UWCA,0.1515
3.0,0.1,1,OFDMA,0.12385416666666667
3.0,0.1,1,UWCA,0.12385416666666667
4.0,0.1,1,OFDMA,0.0989375
4.0,0.1,1,UWCA,0.0989375
5.0,0.1,1,OFDMA,0.07990104166666667
5.0,0.1,1,UWCA,0.07990104166666667
6.0,0.1,1,OFDMA,0.06413802083333334
6.0,0.1,1,UWCA,0.06413802083333334
7.0,0.1,1,OFDMA,0.0513046875
7.0,0.1,1,UWCA,0.0513046875
8.0,0.1,1,OFDMA,0.041041666666666664
8.0,0.1,1,UWCA,0.041041666666666664
9.0,0.1,1,OFDMA,0.032421875
9.0,0.1,1,UWCA,0.032421875
10.0,0.1,1,OFDMA,0.026309895833333333
10.0,0.1,1,UWCA,0.026309895833333333
11.0,0.1,1,OFDMA,0.020497395833333334
11.0,0.1,1,UWCA,0.020497395833333334
12.0,0.1,1,OFDMA,0.016653645833333335
12.0,0.1,1,UWCA,0.016653645833333335
13.0,0.1,1,OFDMA,0.0129453125
13.0,0.1,1,UWCA,0.0129453125
14.0,0.1,1,OFDMA,0.010658854166666667
14.0,0.1,1,UWCA,0.010658854166666667
15.0,0.1,1,OFDMA,0.008330729166666667
15.0,0.1,1,UWCA,0.008330729166666667
16.0,0.1,1,OFDMA,0.006502604166666667
16.0,0.1,1,UWCA,0.006502604166666667
17.0,0.1,1,OFDMA,0.005291666666666667
17.0,0.1,1,UWCA,0.005291666666666667
18.0,0.1,1,OFDMA,0.0040963541666666665
18.0,0.1,1,UWCA,0.0040963541666666665
19.0,0.1,1,OFDMA,0.003395833333333333
19.0,0.1,1,UWCA,0.003395833333333333
20.0,0.1,1,OFDMA,0.0026276041666666665
20.0,0.1,1,UWCA,0.0026276041666666665
0.0,0.1,2,OFDMA,0.49913671875
0.0,0.1,2,UWCA,0.49909635416666664
1.0,0.1,2,OFDMA,0.43283333333333335
1.0,0.1,2,UWCA,0.4327591145833333
2.0,0.1,2,OFDMA,0.3705104166666667
2.0,0.1,2,UWCA,0.3704921875
3.0,0.1,2,OFDMA,0.31485677083333335
3.0,0.1,2,UWCA,0.31479557291666665
4.0,0.1,2,OFDMA,0.2635078125
4.0,0.1,2,UWCA,0.26355208333333335
5.0,0.1,2,OFDMA,0.2196953125
5.0,0.1,2,UWCA,0.21966927083333335
6.0,0.1,2,OFDMA,0.18078255208333333
6.0,0.1,2,UWCA,0.18079296875
7.0,0.1,2,OFDMA,0.148890625
7.0,0.1,2,UWCA,0.14896354166666667
8.0,0.1,2,OFDMA,0.12160416666666667
8.0,0.1,2,UWCA,0.1215390625
9.0,0.1,2,OFDMA,0.09804166666666667
9.0,0.1,2,UWCA,0.09801432291666666
10.0,0.1,2,OFDMA,0.07925390625
10.0,0.1,2,UWCA,0.07925911458333333
11.0,0.1,2,OFDMA,0.06420833333333334
11.0,0.1,2,UWCA,0.06422265625
12.0,0.1,2,OFDMA,0.05216666666666667
12.0,0.1,2,UWCA,0.05216276041666667
13.0,0.1,2,OFDMA,0.04181119791666667
13.0,0.1,2,UWCA,0.04178385416666667
14.0,0.1,2,OFDMA,0.03341015625
14.0,0.1,2,UWCA,0.033430989583333334
15.0,0.1,2,OFDMA,0.02662890625
15.0,0.1,2,UWCA,0.02664453125
16.0,0.1,2,OFDMA,0.021385416666666667
16.0,0.1,2,UWCA,0.021393229166666666
17.0,0.1,2,OFDMA,0.016815104166666667
17.0,0.1,2,UWCA,0.016833333333333332
18.0,0.1,2,OFDMA,0.01357421875
18.0,0.1,2,UWCA,0.013572916666666667
19.0,0.1,2,OFDMA,0.010752604166666667
19.0,0.1,2,UWCA,0.01075390625
20.0,0.1,2,OFDMA,0.008647135416666667
20.0,0.1,2,UWCA,0.00865234375
0.0,0.1,3,OFDMA,0.7359644097222222
0.0,0.1,3,UWCA,0.7357881944444434
1.0,0.1,3,OFDMA,0.6838758680555558
1.0,0.1,3,UWCA,0.6837343749999996
2.0,0.1,3,OFDMA,0.6291024305555565
2.0,0.1,3,UWCA,0.6289392361111122
3.0,0.1,3,OFDMA,0.5744965277777774
3.0,0.1,3,UWCA,0.5743038194444448
4.0,0.1,3,OFDMA,0.5179739583333329
4.0,0.1,3,UWCA,0.5178081597222208
5.0,0.1,3,OFDMA,0.46559461805555596
5.0,0.1,3,UWCA,0.4653567708333337
6.0,0.1,3,OFDMA,0.41591319444444413
6.0,0.1,3,UWCA,0.4157847222222222
7.0,0.1,3,OFDMA,0.36913541666666666
7.0,0.1,3,UWCA,0.3688446180555554
8.0,0.1,3,OFDMA,0.32639843750000047
8.0,0.1,3,UWCA,0.326183159722222
9.0,0.1,3,OFDMA,0.28714409722222206
9.0,0.1,3,UWCA,0.2870355902777776
10.0,0.1,3,OFDMA,0.2535763888888888
10.0,0.1,3,UWCA,0.2534088541666671
11.0,0.1,3,OFDMA,0.223546875
11.0,0.1,3,UWCA,0.22335503472222196
12.0,0.1,3,OFDMA,0.19782204861111136
12.0,0.1,3,UWCA,0.19759982638888887
13.0,0.1,3,OFDMA,0.17522916666666644
13.0,0.1,3,UWCA,0.17518576388888885
14.0,0.1,3,OFDMA,0.15410329861111113
14.0,0.1,3,UWCA,0.15395312499999986
15.0,0.1,3,OFDMA,0.13812326388888907
15.0,0.1,3,UWCA,0.13803645833333342
16.0,0.1,3,OFDMA,0.12451475694444467
16.0,0.1,3,UWCA,0.12446527777777763
17.0,0.1,3,OFDMA,0.11201996527777773
17.0,0.1,3,UWCA,0.11193315972222223
18.0,0.1,3,OFDMA,0.1027942708333334
18.0,0.1,3,UWCA,0.10274131944444452
19.0,0.1,3,OFDMA,0.09420225694444455
19.0,0.1,3,UWCA,0.09416319444444435
20.0,0.1,3,OFDMA,0.08725520833333329
20.0,0.1,3,UWCA,0.08711371527777766
0.0,0.1,4,OFDMA,0.8626236979166667
0.0,0.1,4,UWCA,0.8626126302083333
1.0,0.1,4,OFDMA,0.8311321614583334
1.0,0.1,4,UWCA,0.83095703125
2.0,0.1,4,OFDMA,0.797025390625
2.0,0.1,4,UWCA,0.7968372395833333
3.0,0.1,4,OFDMA,0.7602115885416667
3.0,0.1,4,UWCA,0.760072265625
4.0,0.1,4,OFDMA,0.7220416666666667
4.0,0.1,4,UWCA,0.721810546875
5.0,0.1,4,OFDMA,0.68299609375
5.0,0.1,4,UWCA,0.6826901041666666
6.0,0.1,4,OFDMA,0.645142578125
6.0,0.1,4,UWCA,0.6448776041666666
7.0,0.1,4,OFDMA,0.607173828125
7.0,0.1,4,UWCA,0.6068268229166667
8.0,0.1,4,OFDMA,0.5727662760416666
8.0,0.1,4,UWCA,0.572451171875
9.0,0.1,4,OFDMA,0.5384420572916667
9.0,0.1,4,UWCA,0.5380950520833333
10.0,0.1,4,OFDMA,0.507392578125
10.0,0.1,4,UWCA,0.5070091145833333
11.0,0.1,4,OFDMA,0.4781666666666667
11.0,0.1,4,UWCA,0.477685546875
12.0,0.1,4,OFDMA,0.452376953125
12.0,0.1,4,UWCA,0.45219205729166667
13.0,0.1,4,OFDMA,0.4290950520833333
13.0,0.1,4,UWCA,0.42871354166666664
14.0,0.1,4,OFDMA,0.40861458333333334
14.0,0.1,4,UWCA,0.40822265625
15.0,0.1,4,OFDMA,0.38940234375
15.0,0.1,4,UWCA,0.3890794270833333
16.0,0.1,4,OFDMA,0.3740703125
16.0,0.1,4,UWCA,0.37369401041666667
17.0,0.1,4,OFDMA,0.359716796875
17.0,0.1,4,UWCA,0.35935416666666664
18.0,0.1,4,OFDMA,0.34780859375
18.0,0.1,4,UWCA,0.3475540364583333
19.0,0.1,4,OFDMA,0.3375989583333333
19.0,0.1,4,UWCA,0.337220703125
20.0,0.1,4,OFDMA,0.32880403645833334
20.0,0.1,4,UWCA,0.328451171875
1 snr_db beta U method ser
2 0.0 0.9 1 OFDMA 0.2277109375
3 0.0 0.9 1 UWCA 0.2277109375
4 1.0 0.9 1 OFDMA 0.186171875
5 1.0 0.9 1 UWCA 0.186171875
6 2.0 0.9 1 OFDMA 0.15054427083333333
7 2.0 0.9 1 UWCA 0.15054427083333333
8 3.0 0.9 1 OFDMA 0.12235416666666667
9 3.0 0.9 1 UWCA 0.12235416666666667
10 4.0 0.9 1 OFDMA 0.09946875
11 4.0 0.9 1 UWCA 0.09946875
12 5.0 0.9 1 OFDMA 0.0797890625
13 5.0 0.9 1 UWCA 0.0797890625
14 6.0 0.9 1 OFDMA 0.06358072916666667
15 6.0 0.9 1 UWCA 0.06358072916666667
16 7.0 0.9 1 OFDMA 0.05181510416666667
17 7.0 0.9 1 UWCA 0.05181510416666667
18 8.0 0.9 1 OFDMA 0.04116145833333333
19 8.0 0.9 1 UWCA 0.04116145833333333
20 9.0 0.9 1 OFDMA 0.032716145833333335
21 9.0 0.9 1 UWCA 0.032716145833333335
22 10.0 0.9 1 OFDMA 0.026020833333333333
23 10.0 0.9 1 UWCA 0.026020833333333333
24 11.0 0.9 1 OFDMA 0.021356770833333334
25 11.0 0.9 1 UWCA 0.021356770833333334
26 12.0 0.9 1 OFDMA 0.015979166666666666
27 12.0 0.9 1 UWCA 0.015979166666666666
28 13.0 0.9 1 OFDMA 0.013575520833333334
29 13.0 0.9 1 UWCA 0.013575520833333334
30 14.0 0.9 1 OFDMA 0.010421875
31 14.0 0.9 1 UWCA 0.010421875
32 15.0 0.9 1 OFDMA 0.008375
33 15.0 0.9 1 UWCA 0.008375
34 16.0 0.9 1 OFDMA 0.006455729166666667
35 16.0 0.9 1 UWCA 0.006455729166666667
36 17.0 0.9 1 OFDMA 0.005231770833333333
37 17.0 0.9 1 UWCA 0.005231770833333333
38 18.0 0.9 1 OFDMA 0.004354166666666667
39 18.0 0.9 1 UWCA 0.004354166666666667
40 19.0 0.9 1 OFDMA 0.003380208333333333
41 19.0 0.9 1 UWCA 0.003380208333333333
42 20.0 0.9 1 OFDMA 0.0026927083333333334
43 20.0 0.9 1 UWCA 0.0026927083333333334
44 0.0 0.9 2 OFDMA 0.48298307291666664
45 0.0 0.9 2 UWCA 0.27999869791666665
46 1.0 0.9 2 OFDMA 0.41434765625
47 1.0 0.9 2 UWCA 0.23050651041666667
48 2.0 0.9 2 OFDMA 0.35125260416666665
49 2.0 0.9 2 UWCA 0.1897265625
50 3.0 0.9 2 OFDMA 0.29434635416666666
51 3.0 0.9 2 UWCA 0.1546640625
52 4.0 0.9 2 OFDMA 0.24390104166666668
53 4.0 0.9 2 UWCA 0.1256484375
54 5.0 0.9 2 OFDMA 0.20093880208333334
55 5.0 0.9 2 UWCA 0.10131510416666667
56 6.0 0.9 2 OFDMA 0.16413411458333332
57 6.0 0.9 2 UWCA 0.08115234375
58 7.0 0.9 2 OFDMA 0.13369270833333333
59 7.0 0.9 2 UWCA 0.06594140625
60 8.0 0.9 2 OFDMA 0.10834375
61 8.0 0.9 2 UWCA 0.05243619791666667
62 9.0 0.9 2 OFDMA 0.08767447916666667
63 9.0 0.9 2 UWCA 0.04204817708333333
64 10.0 0.9 2 OFDMA 0.06969791666666666
65 10.0 0.9 2 UWCA 0.033365885416666664
66 11.0 0.9 2 OFDMA 0.05624609375
67 11.0 0.9 2 UWCA 0.026709635416666665
68 12.0 0.9 2 OFDMA 0.045184895833333336
69 12.0 0.9 2 UWCA 0.021680989583333334
70 13.0 0.9 2 OFDMA 0.03581119791666667
71 13.0 0.9 2 UWCA 0.016889322916666668
72 14.0 0.9 2 OFDMA 0.029057291666666665
73 14.0 0.9 2 UWCA 0.013725260416666666
74 15.0 0.9 2 OFDMA 0.02318359375
75 15.0 0.9 2 UWCA 0.010756510416666667
76 16.0 0.9 2 OFDMA 0.01820703125
77 16.0 0.9 2 UWCA 0.008615885416666667
78 17.0 0.9 2 OFDMA 0.014565104166666667
79 17.0 0.9 2 UWCA 0.0068671875
80 18.0 0.9 2 OFDMA 0.011600260416666666
81 18.0 0.9 2 UWCA 0.00551953125
82 19.0 0.9 2 OFDMA 0.009049479166666667
83 19.0 0.9 2 UWCA 0.004247395833333333
84 20.0 0.9 2 OFDMA 0.007338541666666667
85 20.0 0.9 2 UWCA 0.003494791666666667
86 0.0 0.9 3 OFDMA 0.7357656250000003
87 0.0 0.9 3 UWCA 0.30170746527777786
88 1.0 0.9 3 OFDMA 0.6863949652777775
89 1.0 0.9 3 UWCA 0.2504392361111111
90 2.0 0.9 3 OFDMA 0.6347604166666653
91 2.0 0.9 3 UWCA 0.20678732638888933
92 3.0 0.9 3 OFDMA 0.5824887152777778
93 3.0 0.9 3 UWCA 0.16832638888888882
94 4.0 0.9 3 OFDMA 0.5298906250000003
95 4.0 0.9 3 UWCA 0.1375590277777777
96 5.0 0.9 3 OFDMA 0.47926996527777777
97 5.0 0.9 3 UWCA 0.11143576388888887
98 6.0 0.9 3 OFDMA 0.4304539930555558
99 6.0 0.9 3 UWCA 0.08954427083333347
100 7.0 0.9 3 OFDMA 0.38406684027777804
101 7.0 0.9 3 UWCA 0.07243142361111098
102 8.0 0.9 3 OFDMA 0.34143576388888847
103 8.0 0.9 3 UWCA 0.05816579861111104
104 9.0 0.9 3 OFDMA 0.30193142361111125
105 9.0 0.9 3 UWCA 0.04592013888888885
106 10.0 0.9 3 OFDMA 0.26722135416666665
107 10.0 0.9 3 UWCA 0.037015624999999976
108 11.0 0.9 3 OFDMA 0.23677170138888876
109 11.0 0.9 3 UWCA 0.02993315972222221
110 12.0 0.9 3 OFDMA 0.20839843749999995
111 12.0 0.9 3 UWCA 0.023803819444444475
112 13.0 0.9 3 OFDMA 0.1833237847222221
113 13.0 0.9 3 UWCA 0.018681423611111116
114 14.0 0.9 3 OFDMA 0.1632864583333333
115 14.0 0.9 3 UWCA 0.015031250000000036
116 15.0 0.9 3 OFDMA 0.1451927083333333
117 15.0 0.9 3 UWCA 0.012011284722222248
118 16.0 0.9 3 OFDMA 0.13011024305555544
119 16.0 0.9 3 UWCA 0.009515624999999995
120 17.0 0.9 3 OFDMA 0.11676909722222234
121 17.0 0.9 3 UWCA 0.007711805555555572
122 18.0 0.9 3 OFDMA 0.10585677083333331
123 18.0 0.9 3 UWCA 0.006151041666666654
124 19.0 0.9 3 OFDMA 0.09616927083333313
125 19.0 0.9 3 UWCA 0.00486892361111111
126 20.0 0.9 3 OFDMA 0.08875954861111118
127 20.0 0.9 3 UWCA 0.003828125000000005
128 0.0 0.9 4 OFDMA 0.8781588541666666
129 0.0 0.9 4 UWCA 0.315431640625
130 1.0 0.9 4 OFDMA 0.843181640625
131 1.0 0.9 4 UWCA 0.262328125
132 2.0 0.9 4 OFDMA 0.804537109375
133 2.0 0.9 4 UWCA 0.21639127604166666
134 3.0 0.9 4 OFDMA 0.76352734375
135 3.0 0.9 4 UWCA 0.177685546875
136 4.0 0.9 4 OFDMA 0.7189934895833333
137 4.0 0.9 4 UWCA 0.14457486979166667
138 5.0 0.9 4 OFDMA 0.6737454427083334
139 5.0 0.9 4 UWCA 0.11712369791666667
140 6.0 0.9 4 OFDMA 0.6293020833333334
141 6.0 0.9 4 UWCA 0.09453580729166666
142 7.0 0.9 4 OFDMA 0.5850514322916667
143 7.0 0.9 4 UWCA 0.07621223958333333
144 8.0 0.9 4 OFDMA 0.5429192708333334
145 8.0 0.9 4 UWCA 0.060830078125
146 9.0 0.9 4 OFDMA 0.50422265625
147 9.0 0.9 4 UWCA 0.04935611979166667
148 10.0 0.9 4 OFDMA 0.46758268229166666
149 10.0 0.9 4 UWCA 0.039248046875
150 11.0 0.9 4 OFDMA 0.43517578125
151 11.0 0.9 4 UWCA 0.031373046875
152 12.0 0.9 4 OFDMA 0.4051640625
153 12.0 0.9 4 UWCA 0.024932291666666665
154 13.0 0.9 4 OFDMA 0.3789290364583333
155 13.0 0.9 4 UWCA 0.019959635416666666
156 14.0 0.9 4 OFDMA 0.3555846354166667
157 14.0 0.9 4 UWCA 0.01582421875
158 15.0 0.9 4 OFDMA 0.335716796875
159 15.0 0.9 4 UWCA 0.012673828125
160 16.0 0.9 4 OFDMA 0.31779752604166667
161 16.0 0.9 4 UWCA 0.010057942708333333
162 17.0 0.9 4 OFDMA 0.302654296875
163 17.0 0.9 4 UWCA 0.00795703125
164 18.0 0.9 4 OFDMA 0.2898072916666667
165 18.0 0.9 4 UWCA 0.006319010416666666
166 19.0 0.9 4 OFDMA 0.27835221354166667
167 19.0 0.9 4 UWCA 0.00501171875
168 20.0 0.9 4 OFDMA 0.269482421875
169 20.0 0.9 4 UWCA 0.003940755208333333
170 0.0 0.5 1 OFDMA 0.22677604166666668
171 0.0 0.5 1 UWCA 0.22677604166666668
172 1.0 0.5 1 OFDMA 0.18487239583333334
173 1.0 0.5 1 UWCA 0.18487239583333334
174 2.0 0.5 1 OFDMA 0.15238541666666666
175 2.0 0.5 1 UWCA 0.15238541666666666
176 3.0 0.5 1 OFDMA 0.1217421875
177 3.0 0.5 1 UWCA 0.1217421875
178 4.0 0.5 1 OFDMA 0.09941145833333333
179 4.0 0.5 1 UWCA 0.09941145833333333
180 5.0 0.5 1 OFDMA 0.07958333333333334
181 5.0 0.5 1 UWCA 0.07958333333333334
182 6.0 0.5 1 OFDMA 0.06379947916666667
183 6.0 0.5 1 UWCA 0.06379947916666667
184 7.0 0.5 1 OFDMA 0.051625
185 7.0 0.5 1 UWCA 0.051625
186 8.0 0.5 1 OFDMA 0.04043229166666667
187 8.0 0.5 1 UWCA 0.04043229166666667
188 9.0 0.5 1 OFDMA 0.0323203125
189 9.0 0.5 1 UWCA 0.0323203125
190 10.0 0.5 1 OFDMA 0.025927083333333333
191 10.0 0.5 1 UWCA 0.025927083333333333
192 11.0 0.5 1 OFDMA 0.020786458333333334
193 11.0 0.5 1 UWCA 0.020786458333333334
194 12.0 0.5 1 OFDMA 0.016614583333333332
195 12.0 0.5 1 UWCA 0.016614583333333332
196 13.0 0.5 1 OFDMA 0.013325520833333333
197 13.0 0.5 1 UWCA 0.013325520833333333
198 14.0 0.5 1 OFDMA 0.010674479166666667
199 14.0 0.5 1 UWCA 0.010674479166666667
200 15.0 0.5 1 OFDMA 0.008361979166666667
201 15.0 0.5 1 UWCA 0.008361979166666667
202 16.0 0.5 1 OFDMA 0.006419270833333333
203 16.0 0.5 1 UWCA 0.006419270833333333
204 17.0 0.5 1 OFDMA 0.005421875
205 17.0 0.5 1 UWCA 0.005421875
206 18.0 0.5 1 OFDMA 0.004419270833333333
207 18.0 0.5 1 UWCA 0.004419270833333333
208 19.0 0.5 1 OFDMA 0.0032708333333333335
209 19.0 0.5 1 UWCA 0.0032708333333333335
210 20.0 0.5 1 OFDMA 0.00253125
211 20.0 0.5 1 UWCA 0.00253125
212 0.0 0.5 2 OFDMA 0.49812630208333336
213 0.0 0.5 2 UWCA 0.46684375
214 1.0 0.5 2 OFDMA 0.43124609375
215 1.0 0.5 2 UWCA 0.40104557291666665
216 2.0 0.5 2 OFDMA 0.3684453125
217 2.0 0.5 2 UWCA 0.34113932291666665
218 3.0 0.5 2 OFDMA 0.31294921875
219 3.0 0.5 2 UWCA 0.28744921875
220 4.0 0.5 2 OFDMA 0.2618932291666667
221 4.0 0.5 2 UWCA 0.2390859375
222 5.0 0.5 2 OFDMA 0.2174921875
223 5.0 0.5 2 UWCA 0.19773307291666667
224 6.0 0.5 2 OFDMA 0.18020442708333334
225 6.0 0.5 2 UWCA 0.163140625
226 7.0 0.5 2 OFDMA 0.14807421875
227 7.0 0.5 2 UWCA 0.13353776041666668
228 8.0 0.5 2 OFDMA 0.120546875
229 8.0 0.5 2 UWCA 0.10822786458333333
230 9.0 0.5 2 OFDMA 0.09825911458333333
231 9.0 0.5 2 UWCA 0.08794661458333333
232 10.0 0.5 2 OFDMA 0.07884114583333333
233 10.0 0.5 2 UWCA 0.07050911458333334
234 11.0 0.5 2 OFDMA 0.06336979166666666
235 11.0 0.5 2 UWCA 0.05633463541666667
236 12.0 0.5 2 OFDMA 0.05137760416666667
237 12.0 0.5 2 UWCA 0.045634114583333336
238 13.0 0.5 2 OFDMA 0.040665364583333335
239 13.0 0.5 2 UWCA 0.03613932291666667
240 14.0 0.5 2 OFDMA 0.03282291666666667
241 14.0 0.5 2 UWCA 0.02910546875
242 15.0 0.5 2 OFDMA 0.026138020833333334
243 15.0 0.5 2 UWCA 0.023204427083333333
244 16.0 0.5 2 OFDMA 0.02068359375
245 16.0 0.5 2 UWCA 0.018377604166666665
246 17.0 0.5 2 OFDMA 0.0166796875
247 17.0 0.5 2 UWCA 0.014713541666666666
248 18.0 0.5 2 OFDMA 0.013592447916666667
249 18.0 0.5 2 UWCA 0.012067708333333333
250 19.0 0.5 2 OFDMA 0.010821614583333333
251 19.0 0.5 2 UWCA 0.009576822916666667
252 20.0 0.5 2 OFDMA 0.0086328125
253 20.0 0.5 2 UWCA 0.007565104166666667
254 0.0 0.5 3 OFDMA 0.7357057291666653
255 0.0 0.5 3 UWCA 0.6688385416666659
256 1.0 0.5 3 OFDMA 0.6836770833333334
257 1.0 0.5 3 UWCA 0.6091597222222226
258 2.0 0.5 3 OFDMA 0.6284079861111111
259 2.0 0.5 3 UWCA 0.548318576388889
260 3.0 0.5 3 OFDMA 0.5731371527777781
261 3.0 0.5 3 UWCA 0.4887960069444456
262 4.0 0.5 3 OFDMA 0.5188046874999994
263 4.0 0.5 3 UWCA 0.4321979166666668
264 5.0 0.5 3 OFDMA 0.4656076388888891
265 5.0 0.5 3 UWCA 0.3781883680555565
266 6.0 0.5 3 OFDMA 0.41499479166666636
267 6.0 0.5 3 UWCA 0.3288480902777776
268 7.0 0.5 3 OFDMA 0.36824218750000015
269 7.0 0.5 3 UWCA 0.28369010416666696
270 8.0 0.5 3 OFDMA 0.32604600694444463
271 8.0 0.5 3 UWCA 0.2442039930555557
272 9.0 0.5 3 OFDMA 0.28732899305555554
273 9.0 0.5 3 UWCA 0.20949305555555559
274 10.0 0.5 3 OFDMA 0.254361111111111
275 10.0 0.5 3 UWCA 0.17940104166666673
276 11.0 0.5 3 OFDMA 0.2233940972222222
277 11.0 0.5 3 UWCA 0.15297916666666672
278 12.0 0.5 3 OFDMA 0.19677864583333357
279 12.0 0.5 3 UWCA 0.13052864583333318
280 13.0 0.5 3 OFDMA 0.1747682291666668
281 13.0 0.5 3 UWCA 0.11215451388888888
282 14.0 0.5 3 OFDMA 0.15551996527777784
283 14.0 0.5 3 UWCA 0.09648263888888882
284 15.0 0.5 3 OFDMA 0.13859375000000004
285 15.0 0.5 3 UWCA 0.08325000000000007
286 16.0 0.5 3 OFDMA 0.12402083333333326
287 16.0 0.5 3 UWCA 0.0724184027777778
288 17.0 0.5 3 OFDMA 0.11267361111111107
289 17.0 0.5 3 UWCA 0.0631432291666666
290 18.0 0.5 3 OFDMA 0.10258767361111107
291 18.0 0.5 3 UWCA 0.05576736111111118
292 19.0 0.5 3 OFDMA 0.09442881944444444
293 19.0 0.5 3 UWCA 0.049179687499999965
294 20.0 0.5 3 OFDMA 0.0873802083333334
295 20.0 0.5 3 UWCA 0.04426736111111111
296 0.0 0.5 4 OFDMA 0.86390625
297 0.0 0.5 4 UWCA 0.7880390625
298 1.0 0.5 4 OFDMA 0.832029296875
299 1.0 0.5 4 UWCA 0.7429388020833333
300 2.0 0.5 4 OFDMA 0.7977337239583333
301 2.0 0.5 4 UWCA 0.6948938802083333
302 3.0 0.5 4 OFDMA 0.7601510416666667
303 3.0 0.5 4 UWCA 0.64496484375
304 4.0 0.5 4 OFDMA 0.722421875
305 4.0 0.5 4 UWCA 0.595236328125
306 5.0 0.5 4 OFDMA 0.683517578125
307 5.0 0.5 4 UWCA 0.5458072916666666
308 6.0 0.5 4 OFDMA 0.6447838541666666
309 6.0 0.5 4 UWCA 0.4987213541666667
310 7.0 0.5 4 OFDMA 0.6064576822916666
311 7.0 0.5 4 UWCA 0.453876953125
312 8.0 0.5 4 OFDMA 0.5705520833333333
313 8.0 0.5 4 UWCA 0.412064453125
314 9.0 0.5 4 OFDMA 0.536923828125
315 9.0 0.5 4 UWCA 0.3736608072916667
316 10.0 0.5 4 OFDMA 0.50454296875
317 10.0 0.5 4 UWCA 0.33835807291666664
318 11.0 0.5 4 OFDMA 0.476373046875
319 11.0 0.5 4 UWCA 0.30787044270833336
320 12.0 0.5 4 OFDMA 0.450078125
321 12.0 0.5 4 UWCA 0.280119140625
322 13.0 0.5 4 OFDMA 0.42621809895833335
323 13.0 0.5 4 UWCA 0.2567434895833333
324 14.0 0.5 4 OFDMA 0.4048450520833333
325 14.0 0.5 4 UWCA 0.23559049479166666
326 15.0 0.5 4 OFDMA 0.3863958333333333
327 15.0 0.5 4 UWCA 0.21690234375
328 16.0 0.5 4 OFDMA 0.3704524739583333
329 16.0 0.5 4 UWCA 0.20187565104166666
330 17.0 0.5 4 OFDMA 0.35684765625
331 17.0 0.5 4 UWCA 0.18953190104166667
332 18.0 0.5 4 OFDMA 0.343931640625
333 18.0 0.5 4 UWCA 0.17797526041666667
334 19.0 0.5 4 OFDMA 0.333697265625
335 19.0 0.5 4 UWCA 0.16815950520833334
336 20.0 0.5 4 OFDMA 0.32444466145833334
337 20.0 0.5 4 UWCA 0.16032161458333333
338 0.0 0.1 1 OFDMA 0.22649479166666667
339 0.0 0.1 1 UWCA 0.22649479166666667
340 1.0 0.1 1 OFDMA 0.18648958333333332
341 1.0 0.1 1 UWCA 0.18648958333333332
342 2.0 0.1 1 OFDMA 0.1515
343 2.0 0.1 1 UWCA 0.1515
344 3.0 0.1 1 OFDMA 0.12385416666666667
345 3.0 0.1 1 UWCA 0.12385416666666667
346 4.0 0.1 1 OFDMA 0.0989375
347 4.0 0.1 1 UWCA 0.0989375
348 5.0 0.1 1 OFDMA 0.07990104166666667
349 5.0 0.1 1 UWCA 0.07990104166666667
350 6.0 0.1 1 OFDMA 0.06413802083333334
351 6.0 0.1 1 UWCA 0.06413802083333334
352 7.0 0.1 1 OFDMA 0.0513046875
353 7.0 0.1 1 UWCA 0.0513046875
354 8.0 0.1 1 OFDMA 0.041041666666666664
355 8.0 0.1 1 UWCA 0.041041666666666664
356 9.0 0.1 1 OFDMA 0.032421875
357 9.0 0.1 1 UWCA 0.032421875
358 10.0 0.1 1 OFDMA 0.026309895833333333
359 10.0 0.1 1 UWCA 0.026309895833333333
360 11.0 0.1 1 OFDMA 0.020497395833333334
361 11.0 0.1 1 UWCA 0.020497395833333334
362 12.0 0.1 1 OFDMA 0.016653645833333335
363 12.0 0.1 1 UWCA 0.016653645833333335
364 13.0 0.1 1 OFDMA 0.0129453125
365 13.0 0.1 1 UWCA 0.0129453125
366 14.0 0.1 1 OFDMA 0.010658854166666667
367 14.0 0.1 1 UWCA 0.010658854166666667
368 15.0 0.1 1 OFDMA 0.008330729166666667
369 15.0 0.1 1 UWCA 0.008330729166666667
370 16.0 0.1 1 OFDMA 0.006502604166666667
371 16.0 0.1 1 UWCA 0.006502604166666667
372 17.0 0.1 1 OFDMA 0.005291666666666667
373 17.0 0.1 1 UWCA 0.005291666666666667
374 18.0 0.1 1 OFDMA 0.0040963541666666665
375 18.0 0.1 1 UWCA 0.0040963541666666665
376 19.0 0.1 1 OFDMA 0.003395833333333333
377 19.0 0.1 1 UWCA 0.003395833333333333
378 20.0 0.1 1 OFDMA 0.0026276041666666665
379 20.0 0.1 1 UWCA 0.0026276041666666665
380 0.0 0.1 2 OFDMA 0.49913671875
381 0.0 0.1 2 UWCA 0.49909635416666664
382 1.0 0.1 2 OFDMA 0.43283333333333335
383 1.0 0.1 2 UWCA 0.4327591145833333
384 2.0 0.1 2 OFDMA 0.3705104166666667
385 2.0 0.1 2 UWCA 0.3704921875
386 3.0 0.1 2 OFDMA 0.31485677083333335
387 3.0 0.1 2 UWCA 0.31479557291666665
388 4.0 0.1 2 OFDMA 0.2635078125
389 4.0 0.1 2 UWCA 0.26355208333333335
390 5.0 0.1 2 OFDMA 0.2196953125
391 5.0 0.1 2 UWCA 0.21966927083333335
392 6.0 0.1 2 OFDMA 0.18078255208333333
393 6.0 0.1 2 UWCA 0.18079296875
394 7.0 0.1 2 OFDMA 0.148890625
395 7.0 0.1 2 UWCA 0.14896354166666667
396 8.0 0.1 2 OFDMA 0.12160416666666667
397 8.0 0.1 2 UWCA 0.1215390625
398 9.0 0.1 2 OFDMA 0.09804166666666667
399 9.0 0.1 2 UWCA 0.09801432291666666
400 10.0 0.1 2 OFDMA 0.07925390625
401 10.0 0.1 2 UWCA 0.07925911458333333
402 11.0 0.1 2 OFDMA 0.06420833333333334
403 11.0 0.1 2 UWCA 0.06422265625
404 12.0 0.1 2 OFDMA 0.05216666666666667
405 12.0 0.1 2 UWCA 0.05216276041666667
406 13.0 0.1 2 OFDMA 0.04181119791666667
407 13.0 0.1 2 UWCA 0.04178385416666667
408 14.0 0.1 2 OFDMA 0.03341015625
409 14.0 0.1 2 UWCA 0.033430989583333334
410 15.0 0.1 2 OFDMA 0.02662890625
411 15.0 0.1 2 UWCA 0.02664453125
412 16.0 0.1 2 OFDMA 0.021385416666666667
413 16.0 0.1 2 UWCA 0.021393229166666666
414 17.0 0.1 2 OFDMA 0.016815104166666667
415 17.0 0.1 2 UWCA 0.016833333333333332
416 18.0 0.1 2 OFDMA 0.01357421875
417 18.0 0.1 2 UWCA 0.013572916666666667
418 19.0 0.1 2 OFDMA 0.010752604166666667
419 19.0 0.1 2 UWCA 0.01075390625
420 20.0 0.1 2 OFDMA 0.008647135416666667
421 20.0 0.1 2 UWCA 0.00865234375
422 0.0 0.1 3 OFDMA 0.7359644097222222
423 0.0 0.1 3 UWCA 0.7357881944444434
424 1.0 0.1 3 OFDMA 0.6838758680555558
425 1.0 0.1 3 UWCA 0.6837343749999996
426 2.0 0.1 3 OFDMA 0.6291024305555565
427 2.0 0.1 3 UWCA 0.6289392361111122
428 3.0 0.1 3 OFDMA 0.5744965277777774
429 3.0 0.1 3 UWCA 0.5743038194444448
430 4.0 0.1 3 OFDMA 0.5179739583333329
431 4.0 0.1 3 UWCA 0.5178081597222208
432 5.0 0.1 3 OFDMA 0.46559461805555596
433 5.0 0.1 3 UWCA 0.4653567708333337
434 6.0 0.1 3 OFDMA 0.41591319444444413
435 6.0 0.1 3 UWCA 0.4157847222222222
436 7.0 0.1 3 OFDMA 0.36913541666666666
437 7.0 0.1 3 UWCA 0.3688446180555554
438 8.0 0.1 3 OFDMA 0.32639843750000047
439 8.0 0.1 3 UWCA 0.326183159722222
440 9.0 0.1 3 OFDMA 0.28714409722222206
441 9.0 0.1 3 UWCA 0.2870355902777776
442 10.0 0.1 3 OFDMA 0.2535763888888888
443 10.0 0.1 3 UWCA 0.2534088541666671
444 11.0 0.1 3 OFDMA 0.223546875
445 11.0 0.1 3 UWCA 0.22335503472222196
446 12.0 0.1 3 OFDMA 0.19782204861111136
447 12.0 0.1 3 UWCA 0.19759982638888887
448 13.0 0.1 3 OFDMA 0.17522916666666644
449 13.0 0.1 3 UWCA 0.17518576388888885
450 14.0 0.1 3 OFDMA 0.15410329861111113
451 14.0 0.1 3 UWCA 0.15395312499999986
452 15.0 0.1 3 OFDMA 0.13812326388888907
453 15.0 0.1 3 UWCA 0.13803645833333342
454 16.0 0.1 3 OFDMA 0.12451475694444467
455 16.0 0.1 3 UWCA 0.12446527777777763
456 17.0 0.1 3 OFDMA 0.11201996527777773
457 17.0 0.1 3 UWCA 0.11193315972222223
458 18.0 0.1 3 OFDMA 0.1027942708333334
459 18.0 0.1 3 UWCA 0.10274131944444452
460 19.0 0.1 3 OFDMA 0.09420225694444455
461 19.0 0.1 3 UWCA 0.09416319444444435
462 20.0 0.1 3 OFDMA 0.08725520833333329
463 20.0 0.1 3 UWCA 0.08711371527777766
464 0.0 0.1 4 OFDMA 0.8626236979166667
465 0.0 0.1 4 UWCA 0.8626126302083333
466 1.0 0.1 4 OFDMA 0.8311321614583334
467 1.0 0.1 4 UWCA 0.83095703125
468 2.0 0.1 4 OFDMA 0.797025390625
469 2.0 0.1 4 UWCA 0.7968372395833333
470 3.0 0.1 4 OFDMA 0.7602115885416667
471 3.0 0.1 4 UWCA 0.760072265625
472 4.0 0.1 4 OFDMA 0.7220416666666667
473 4.0 0.1 4 UWCA 0.721810546875
474 5.0 0.1 4 OFDMA 0.68299609375
475 5.0 0.1 4 UWCA 0.6826901041666666
476 6.0 0.1 4 OFDMA 0.645142578125
477 6.0 0.1 4 UWCA 0.6448776041666666
478 7.0 0.1 4 OFDMA 0.607173828125
479 7.0 0.1 4 UWCA 0.6068268229166667
480 8.0 0.1 4 OFDMA 0.5727662760416666
481 8.0 0.1 4 UWCA 0.572451171875
482 9.0 0.1 4 OFDMA 0.5384420572916667
483 9.0 0.1 4 UWCA 0.5380950520833333
484 10.0 0.1 4 OFDMA 0.507392578125
485 10.0 0.1 4 UWCA 0.5070091145833333
486 11.0 0.1 4 OFDMA 0.4781666666666667
487 11.0 0.1 4 UWCA 0.477685546875
488 12.0 0.1 4 OFDMA 0.452376953125
489 12.0 0.1 4 UWCA 0.45219205729166667
490 13.0 0.1 4 OFDMA 0.4290950520833333
491 13.0 0.1 4 UWCA 0.42871354166666664
492 14.0 0.1 4 OFDMA 0.40861458333333334
493 14.0 0.1 4 UWCA 0.40822265625
494 15.0 0.1 4 OFDMA 0.38940234375
495 15.0 0.1 4 UWCA 0.3890794270833333
496 16.0 0.1 4 OFDMA 0.3740703125
497 16.0 0.1 4 UWCA 0.37369401041666667
498 17.0 0.1 4 OFDMA 0.359716796875
499 17.0 0.1 4 UWCA 0.35935416666666664
500 18.0 0.1 4 OFDMA 0.34780859375
501 18.0 0.1 4 UWCA 0.3475540364583333
502 19.0 0.1 4 OFDMA 0.3375989583333333
503 19.0 0.1 4 UWCA 0.337220703125
504 20.0 0.1 4 OFDMA 0.32880403645833334
505 20.0 0.1 4 UWCA 0.328451171875
simulation/results/data/u_variation_high.csv
Executable
+89
View File
@@ -0,0 +1,89 @@
snr_db,U,method,ser
0.0,1,OFDMA,0.2235
0.0,1,UWCA,0.2235
2.0,1,OFDMA,0.152625
2.0,1,UWCA,0.152625
4.0,1,OFDMA,0.098
4.0,1,UWCA,0.098
6.0,1,OFDMA,0.066
6.0,1,UWCA,0.066
8.0,1,OFDMA,0.03903125
8.0,1,UWCA,0.03903125
10.0,1,OFDMA,0.02728125
10.0,1,UWCA,0.02728125
12.0,1,OFDMA,0.01659375
12.0,1,UWCA,0.01659375
14.0,1,OFDMA,0.0106875
14.0,1,UWCA,0.0106875
16.0,1,OFDMA,0.00684375
16.0,1,UWCA,0.00684375
18.0,1,OFDMA,0.00378125
18.0,1,UWCA,0.00378125
20.0,1,OFDMA,0.00278125
20.0,1,UWCA,0.00278125
0.0,2,OFDMA,0.4799375
0.0,2,UWCA,0.254578125
2.0,2,OFDMA,0.34384375
2.0,2,UWCA,0.171015625
4.0,2,OFDMA,0.24153125
4.0,2,UWCA,0.112140625
6.0,2,OFDMA,0.1615625
6.0,2,UWCA,0.072234375
8.0,2,OFDMA,0.1063125
8.0,2,UWCA,0.047671875
10.0,2,OFDMA,0.06746875
10.0,2,UWCA,0.029703125
12.0,2,OFDMA,0.043453125
12.0,2,UWCA,0.0193125
14.0,2,OFDMA,0.02896875
14.0,2,UWCA,0.01221875
16.0,2,OFDMA,0.01696875
16.0,2,UWCA,0.00678125
18.0,2,OFDMA,0.0113125
18.0,2,UWCA,0.0048125
20.0,2,OFDMA,0.007203125
20.0,2,UWCA,0.002875
0.0,3,OFDMA,0.7381354166666668
0.0,3,UWCA,0.26159375000000007
2.0,3,OFDMA,0.6407500000000004
2.0,3,UWCA,0.1758645833333334
4.0,3,OFDMA,0.5385625000000005
4.0,3,UWCA,0.11663541666666671
6.0,3,OFDMA,0.43827083333333333
6.0,3,UWCA,0.07547916666666671
8.0,3,OFDMA,0.3494999999999999
8.0,3,UWCA,0.048968750000000005
10.0,3,OFDMA,0.2746979166666666
10.0,3,UWCA,0.031249999999999997
12.0,3,OFDMA,0.21194791666666662
12.0,3,UWCA,0.018739583333333348
14.0,3,OFDMA,0.16338541666666684
14.0,3,UWCA,0.012947916666666661
16.0,3,OFDMA,0.1267187499999999
16.0,3,UWCA,0.007947916666666678
18.0,3,OFDMA,0.10140625000000006
18.0,3,UWCA,0.005479166666666673
20.0,3,OFDMA,0.08287500000000002
20.0,3,UWCA,0.003229166666666662
0.0,4,OFDMA,0.8828515625
0.0,4,UWCA,0.2677265625
2.0,4,OFDMA,0.803703125
2.0,4,UWCA,0.1821328125
4.0,4,OFDMA,0.7105390625
4.0,4,UWCA,0.1198359375
6.0,4,OFDMA,0.611984375
6.0,4,UWCA,0.0781015625
8.0,4,OFDMA,0.52203125
8.0,4,UWCA,0.0500390625
10.0,4,OFDMA,0.4451484375
10.0,4,UWCA,0.031734375
12.0,4,OFDMA,0.381828125
12.0,4,UWCA,0.01996875
14.0,4,OFDMA,0.33459375
14.0,4,UWCA,0.0130390625
16.0,4,OFDMA,0.300125
16.0,4,UWCA,0.0075234375
18.0,4,OFDMA,0.2749921875
18.0,4,UWCA,0.005125
20.0,4,OFDMA,0.256453125
20.0,4,UWCA,0.0032265625
1 snr_db U method ser
2 0.0 1 OFDMA 0.2235
3 0.0 1 UWCA 0.2235
4 2.0 1 OFDMA 0.152625
5 2.0 1 UWCA 0.152625
6 4.0 1 OFDMA 0.098
7 4.0 1 UWCA 0.098
8 6.0 1 OFDMA 0.066
9 6.0 1 UWCA 0.066
10 8.0 1 OFDMA 0.03903125
11 8.0 1 UWCA 0.03903125
12 10.0 1 OFDMA 0.02728125
13 10.0 1 UWCA 0.02728125
14 12.0 1 OFDMA 0.01659375
15 12.0 1 UWCA 0.01659375
16 14.0 1 OFDMA 0.0106875
17 14.0 1 UWCA 0.0106875
18 16.0 1 OFDMA 0.00684375
19 16.0 1 UWCA 0.00684375
20 18.0 1 OFDMA 0.00378125
21 18.0 1 UWCA 0.00378125
22 20.0 1 OFDMA 0.00278125
23 20.0 1 UWCA 0.00278125
24 0.0 2 OFDMA 0.4799375
25 0.0 2 UWCA 0.254578125
26 2.0 2 OFDMA 0.34384375
27 2.0 2 UWCA 0.171015625
28 4.0 2 OFDMA 0.24153125
29 4.0 2 UWCA 0.112140625
30 6.0 2 OFDMA 0.1615625
31 6.0 2 UWCA 0.072234375
32 8.0 2 OFDMA 0.1063125
33 8.0 2 UWCA 0.047671875
34 10.0 2 OFDMA 0.06746875
35 10.0 2 UWCA 0.029703125
36 12.0 2 OFDMA 0.043453125
37 12.0 2 UWCA 0.0193125
38 14.0 2 OFDMA 0.02896875
39 14.0 2 UWCA 0.01221875
40 16.0 2 OFDMA 0.01696875
41 16.0 2 UWCA 0.00678125
42 18.0 2 OFDMA 0.0113125
43 18.0 2 UWCA 0.0048125
44 20.0 2 OFDMA 0.007203125
45 20.0 2 UWCA 0.002875
46 0.0 3 OFDMA 0.7381354166666668
47 0.0 3 UWCA 0.26159375000000007
48 2.0 3 OFDMA 0.6407500000000004
49 2.0 3 UWCA 0.1758645833333334
50 4.0 3 OFDMA 0.5385625000000005
51 4.0 3 UWCA 0.11663541666666671
52 6.0 3 OFDMA 0.43827083333333333
53 6.0 3 UWCA 0.07547916666666671
54 8.0 3 OFDMA 0.3494999999999999
55 8.0 3 UWCA 0.048968750000000005
56 10.0 3 OFDMA 0.2746979166666666
57 10.0 3 UWCA 0.031249999999999997
58 12.0 3 OFDMA 0.21194791666666662
59 12.0 3 UWCA 0.018739583333333348
60 14.0 3 OFDMA 0.16338541666666684
61 14.0 3 UWCA 0.012947916666666661
62 16.0 3 OFDMA 0.1267187499999999
63 16.0 3 UWCA 0.007947916666666678
64 18.0 3 OFDMA 0.10140625000000006
65 18.0 3 UWCA 0.005479166666666673
66 20.0 3 OFDMA 0.08287500000000002
67 20.0 3 UWCA 0.003229166666666662
68 0.0 4 OFDMA 0.8828515625
69 0.0 4 UWCA 0.2677265625
70 2.0 4 OFDMA 0.803703125
71 2.0 4 UWCA 0.1821328125
72 4.0 4 OFDMA 0.7105390625
73 4.0 4 UWCA 0.1198359375
74 6.0 4 OFDMA 0.611984375
75 6.0 4 UWCA 0.0781015625
76 8.0 4 OFDMA 0.52203125
77 8.0 4 UWCA 0.0500390625
78 10.0 4 OFDMA 0.4451484375
79 10.0 4 UWCA 0.031734375
80 12.0 4 OFDMA 0.381828125
81 12.0 4 UWCA 0.01996875
82 14.0 4 OFDMA 0.33459375
83 14.0 4 UWCA 0.0130390625
84 16.0 4 OFDMA 0.300125
85 16.0 4 UWCA 0.0075234375
86 18.0 4 OFDMA 0.2749921875
87 18.0 4 UWCA 0.005125
88 20.0 4 OFDMA 0.256453125
89 20.0 4 UWCA 0.0032265625
simulation/results/data/u_variation_low.csv
Executable
+89
View File
@@ -0,0 +1,89 @@
snr_db,U,method,ser
0.0,1,OFDMA,0.22984375
0.0,1,UWCA,0.22984375
2.0,1,OFDMA,0.1529375
2.0,1,UWCA,0.1529375
4.0,1,OFDMA,0.0995625
4.0,1,UWCA,0.0995625
6.0,1,OFDMA,0.06365625
6.0,1,UWCA,0.06365625
8.0,1,OFDMA,0.04175
8.0,1,UWCA,0.04175
10.0,1,OFDMA,0.02459375
10.0,1,UWCA,0.02459375
12.0,1,OFDMA,0.01634375
12.0,1,UWCA,0.01634375
14.0,1,OFDMA,0.0103125
14.0,1,UWCA,0.0103125
16.0,1,OFDMA,0.00628125
16.0,1,UWCA,0.00628125
18.0,1,OFDMA,0.00425
18.0,1,UWCA,0.00425
20.0,1,OFDMA,0.0026875
20.0,1,UWCA,0.0026875
0.0,2,OFDMA,0.496703125
0.0,2,UWCA,0.496765625
2.0,2,OFDMA,0.370234375
2.0,2,UWCA,0.370171875
4.0,2,OFDMA,0.26465625
4.0,2,UWCA,0.264625
6.0,2,OFDMA,0.181265625
6.0,2,UWCA,0.18125
8.0,2,OFDMA,0.12209375
8.0,2,UWCA,0.1219375
10.0,2,OFDMA,0.07803125
10.0,2,UWCA,0.07809375
12.0,2,OFDMA,0.0506875
12.0,2,UWCA,0.05075
14.0,2,OFDMA,0.033828125
14.0,2,UWCA,0.033828125
16.0,2,OFDMA,0.022125
16.0,2,UWCA,0.022140625
18.0,2,OFDMA,0.0131875
18.0,2,UWCA,0.0131875
20.0,2,OFDMA,0.008953125
20.0,2,UWCA,0.008953125
0.0,3,OFDMA,0.738270833333333
0.0,3,UWCA,0.7380729166666662
2.0,3,OFDMA,0.6300000000000002
2.0,3,UWCA,0.6302395833333332
4.0,3,OFDMA,0.5202291666666672
4.0,3,UWCA,0.5202916666666668
6.0,3,OFDMA,0.4163750000000004
6.0,3,UWCA,0.4163125
8.0,3,OFDMA,0.32642708333333303
8.0,3,UWCA,0.3265729166666665
10.0,3,OFDMA,0.25357291666666665
10.0,3,UWCA,0.25358333333333344
12.0,3,OFDMA,0.19608333333333328
12.0,3,UWCA,0.19605208333333332
14.0,3,OFDMA,0.1555416666666666
14.0,3,UWCA,0.15565625
16.0,3,OFDMA,0.12338541666666662
16.0,3,UWCA,0.12355208333333328
18.0,3,OFDMA,0.10289583333333334
18.0,3,UWCA,0.10288541666666665
20.0,3,OFDMA,0.08652083333333328
20.0,3,UWCA,0.08667708333333325
0.0,4,OFDMA,0.8645703125
0.0,4,UWCA,0.8645
2.0,4,OFDMA,0.79559375
2.0,4,UWCA,0.7958203125
4.0,4,OFDMA,0.722015625
4.0,4,UWCA,0.7218828125
6.0,4,OFDMA,0.6452734375
6.0,4,UWCA,0.645390625
8.0,4,OFDMA,0.5735625
8.0,4,UWCA,0.5735390625
10.0,4,OFDMA,0.5073125
10.0,4,UWCA,0.5071875
12.0,4,OFDMA,0.4519453125
12.0,4,UWCA,0.4520703125
14.0,4,OFDMA,0.4091015625
14.0,4,UWCA,0.4090546875
16.0,4,OFDMA,0.37253125
16.0,4,UWCA,0.3725703125
18.0,4,OFDMA,0.346640625
18.0,4,UWCA,0.34659375
20.0,4,OFDMA,0.3288515625
20.0,4,UWCA,0.3289765625
1 snr_db U method ser
2 0.0 1 OFDMA 0.22984375
3 0.0 1 UWCA 0.22984375
4 2.0 1 OFDMA 0.1529375
5 2.0 1 UWCA 0.1529375
6 4.0 1 OFDMA 0.0995625
7 4.0 1 UWCA 0.0995625
8 6.0 1 OFDMA 0.06365625
9 6.0 1 UWCA 0.06365625
10 8.0 1 OFDMA 0.04175
11 8.0 1 UWCA 0.04175
12 10.0 1 OFDMA 0.02459375
13 10.0 1 UWCA 0.02459375
14 12.0 1 OFDMA 0.01634375
15 12.0 1 UWCA 0.01634375
16 14.0 1 OFDMA 0.0103125
17 14.0 1 UWCA 0.0103125
18 16.0 1 OFDMA 0.00628125
19 16.0 1 UWCA 0.00628125
20 18.0 1 OFDMA 0.00425
21 18.0 1 UWCA 0.00425
22 20.0 1 OFDMA 0.0026875
23 20.0 1 UWCA 0.0026875
24 0.0 2 OFDMA 0.496703125
25 0.0 2 UWCA 0.496765625
26 2.0 2 OFDMA 0.370234375
27 2.0 2 UWCA 0.370171875
28 4.0 2 OFDMA 0.26465625
29 4.0 2 UWCA 0.264625
30 6.0 2 OFDMA 0.181265625
31 6.0 2 UWCA 0.18125
32 8.0 2 OFDMA 0.12209375
33 8.0 2 UWCA 0.1219375
34 10.0 2 OFDMA 0.07803125
35 10.0 2 UWCA 0.07809375
36 12.0 2 OFDMA 0.0506875
37 12.0 2 UWCA 0.05075
38 14.0 2 OFDMA 0.033828125
39 14.0 2 UWCA 0.033828125
40 16.0 2 OFDMA 0.022125
41 16.0 2 UWCA 0.022140625
42 18.0 2 OFDMA 0.0131875
43 18.0 2 UWCA 0.0131875
44 20.0 2 OFDMA 0.008953125
45 20.0 2 UWCA 0.008953125
46 0.0 3 OFDMA 0.738270833333333
47 0.0 3 UWCA 0.7380729166666662
48 2.0 3 OFDMA 0.6300000000000002
49 2.0 3 UWCA 0.6302395833333332
50 4.0 3 OFDMA 0.5202291666666672
51 4.0 3 UWCA 0.5202916666666668
52 6.0 3 OFDMA 0.4163750000000004
53 6.0 3 UWCA 0.4163125
54 8.0 3 OFDMA 0.32642708333333303
55 8.0 3 UWCA 0.3265729166666665
56 10.0 3 OFDMA 0.25357291666666665
57 10.0 3 UWCA 0.25358333333333344
58 12.0 3 OFDMA 0.19608333333333328
59 12.0 3 UWCA 0.19605208333333332
60 14.0 3 OFDMA 0.1555416666666666
61 14.0 3 UWCA 0.15565625
62 16.0 3 OFDMA 0.12338541666666662
63 16.0 3 UWCA 0.12355208333333328
64 18.0 3 OFDMA 0.10289583333333334
65 18.0 3 UWCA 0.10288541666666665
66 20.0 3 OFDMA 0.08652083333333328
67 20.0 3 UWCA 0.08667708333333325
68 0.0 4 OFDMA 0.8645703125
69 0.0 4 UWCA 0.8645
70 2.0 4 OFDMA 0.79559375
71 2.0 4 UWCA 0.7958203125
72 4.0 4 OFDMA 0.722015625
73 4.0 4 UWCA 0.7218828125
74 6.0 4 OFDMA 0.6452734375
75 6.0 4 UWCA 0.645390625
76 8.0 4 OFDMA 0.5735625
77 8.0 4 UWCA 0.5735390625
78 10.0 4 OFDMA 0.5073125
79 10.0 4 UWCA 0.5071875
80 12.0 4 OFDMA 0.4519453125
81 12.0 4 UWCA 0.4520703125
82 14.0 4 OFDMA 0.4091015625
83 14.0 4 UWCA 0.4090546875
84 16.0 4 OFDMA 0.37253125
85 16.0 4 UWCA 0.3725703125
86 18.0 4 OFDMA 0.346640625
87 18.0 4 UWCA 0.34659375
88 20.0 4 OFDMA 0.3288515625
89 20.0 4 UWCA 0.3289765625
simulation/results/realdata_analytic.json
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{
"HIGH": {
"OFDMA": [
0.37861328125,
0.41453125,
0.4473828125,
0.468203125,
0.48638671875,
0.5044140625,
0.51908203125,
0.5286328125,
0.53861328125,
0.54025390625,
0.54203125
],
"SFDMA": [
0.3799609375,
0.41423828125,
0.4475,
0.4698046875,
0.4858203125,
0.503671875,
0.51322265625,
0.528125,
0.5378515625,
0.5383984375,
0.54203125
],
"NOMA-SIC": [
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0.85904296875,
0.843515625,
0.83283203125,
0.82787109375,
0.82078125,
0.81755859375
],
"UWCA (analytical)": [
0.736875,
0.78798828125,
0.82431640625,
0.85056640625,
0.866796875,
0.8809375,
0.89392578125,
0.8988671875,
0.9065234375,
0.90408203125,
0.90732421875
]
},
"LOW": {
"OFDMA": [
0.37962890625,
0.41490234375,
0.436796875,
0.465078125,
0.48669921875,
0.497734375,
0.5064453125,
0.516328125,
0.51921875,
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0.5308984375
],
"SFDMA": [
0.38158203125,
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0.4375,
0.46376953125,
0.48546875,
0.49544921875,
0.5080859375,
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0.52048828125,
0.5258984375,
0.53150390625
],
"NOMA-SIC": [
0.18515625,
0.17927734375,
0.17955078125,
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0.18005859375,
0.177421875,
0.1775390625,
0.1780859375,
0.17841796875,
0.17927734375,
0.1799609375
],
"UWCA (analytical)": [
0.37111328125,
0.403828125,
0.429375,
0.453203125,
0.47552734375,
0.48888671875,
0.49654296875,
0.50271484375,
0.5108203125,
0.51544921875,
0.51802734375
]
},
"MIX": {
"OFDMA": [
0.39236328125,
0.42443359375,
0.45361328125,
0.4771875,
0.4936328125,
0.51189453125,
0.52232421875,
0.5315234375,
0.5384765625,
0.54220703125,
0.55119140625
],
"SFDMA": [
0.38923828125,
0.4246875,
0.449140625,
0.47380859375,
0.49046875,
0.5125390625,
0.5215234375,
0.5321875,
0.5395703125,
0.54419921875,
0.5509375
],
"NOMA-SIC": [
0.40421875,
0.41462890625,
0.42103515625,
0.421875,
0.4278515625,
0.4266015625,
0.42970703125,
0.4303125,
0.43126953125,
0.4305859375,
0.4296875
],
"UWCA (analytical)": [
0.46947265625,
0.50712890625,
0.5417578125,
0.56376953125,
0.581640625,
0.6001171875,
0.611171875,
0.61728515625,
0.62193359375,
0.6252734375,
0.63068359375
]
}
}
simulation/results/realdata_train.json
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{"HIGH": {"OFDMA": [0.37457682291666666, 0.41640625, 0.44375, 0.46725260416666664, 0.4910481770833333, 0.5022135416666667, 0.5194010416666667, 0.5304036458333333, 0.5287109375, 0.5407877604166667, 0.5419921875], "NOMA-SIC": [0.9563802083333334, 0.95771484375, 0.9339518229166667, 0.907421875, 0.8827799479166667, 0.85830078125, 0.84296875, 0.8338216145833334, 0.8255533854166667, 0.8222005208333333, 0.8171875], "UWCA w/o MAML": [0.94453125, 0.963232421875, 0.972119140625, 0.98173828125, 0.9859375, 0.989013671875, 0.991455078125, 0.993310546875, 0.995068359375, 0.9939453125, 0.994677734375], "UWCA w/ MAML": [0.9431640625, 0.9626953125, 0.972607421875, 0.981884765625, 0.984130859375, 0.988818359375, 0.9892578125, 0.991259765625, 0.9927734375, 0.99501953125, 0.995068359375]}, "LOW": {"OFDMA": [0.37897135416666666, 0.41106770833333334, 0.4436848958333333, 0.46432291666666664, 0.48626302083333334, 0.49713541666666666, 0.5108072916666667, 0.5147135416666667, 0.5279296875, 0.52509765625, 0.5271809895833334], "NOMA-SIC": [0.18844401041666667, 0.180859375, 0.179296875, 0.17747395833333332, 0.17744140625, 0.17819010416666667, 0.17724609375, 0.18138020833333332, 0.1818359375, 0.17685546875, 0.17789713541666666], "UWCA w/o MAML": [0.70341796875, 0.739404296875, 0.775390625, 0.7953125, 0.8130859375, 0.833447265625, 0.83916015625, 0.84384765625, 0.85478515625, 0.855419921875, 0.8576171875], "UWCA w/ MAML": [0.708447265625, 0.7513671875, 0.77763671875, 0.798486328125, 0.8185546875, 0.830224609375, 0.835302734375, 0.845654296875, 0.849951171875, 0.852490234375, 0.856103515625]}, "MIX": {"OFDMA": [0.391796875, 0.4224609375, 0.45205078125, 0.47584635416666665, 0.49368489583333336, 0.509375, 0.5220052083333333, 0.5345377604166667, 0.53896484375, 0.5467122395833334, 0.5455403645833333], "NOMA-SIC": [0.4019856770833333, 0.4123046875, 0.42047526041666666, 0.42877604166666666, 0.4279296875, 0.42972005208333336, 0.42815755208333334, 0.43095703125, 0.4246744791666667, 0.4322265625, 0.43089192708333335], "UWCA w/o MAML": [0.662353515625, 0.70087890625, 0.732958984375, 0.753271484375, 0.769873046875, 0.777587890625, 0.78916015625, 0.79892578125, 0.79599609375, 0.80224609375, 0.8044921875], "UWCA w/ MAML": [0.65966796875, 0.7029296875, 0.731689453125, 0.747314453125, 0.769384765625, 0.782958984375, 0.79130859375, 0.79853515625, 0.804541015625, 0.805126953125, 0.805712890625]}}
simulation/results/trained_ASYM.json
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{"scenario": "ASYM", "decoder_only": true, "snr_db": [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0], "maml_ser": [0.8293828125, 0.7532578125, 0.66725, 0.5783828125, 0.49840625, 0.432203125, 0.373875, 0.3330859375, 0.30128125, 0.268140625, 0.2499765625], "joint_ser": [0.83509375, 0.7571953125, 0.67265625, 0.5893359375, 0.5086015625, 0.4405703125, 0.3832734375, 0.339265625, 0.3002890625, 0.27859375, 0.2617265625], "ofdma_ser": [0.8637265625, 0.7960390625, 0.7215625, 0.6468046875, 0.572953125, 0.5053203125, 0.4526484375, 0.40265625, 0.3750859375, 0.3479921875, 0.329109375], "noma_ser": [0.596906, 0.500898, 0.432344, 0.388555, 0.364242, 0.353039, 0.347625, 0.346641, 0.344578, 0.346547, 0.345773], "sfdma_ser": [0.863609, 0.795703, 0.718875, 0.645977, 0.570937, 0.507398, 0.450492, 0.41182, 0.373352, 0.346883, 0.32982], "uwca_analytic_ser": [0.803539, 0.720742, 0.621609, 0.535102, 0.447352, 0.376766, 0.319547, 0.281398, 0.239672, 0.218797, 0.200695]}
simulation/results/trained_HETERO.json
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{"scenario": "HETERO", "decoder_only": true, "snr_db": [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0], "maml_ser": [0.7900546875, 0.6976171875, 0.6054765625, 0.516625, 0.4355234375, 0.3695390625, 0.3141796875, 0.2726796875, 0.2419609375, 0.219640625, 0.2081640625], "joint_ser": [0.7938046875, 0.7039140625, 0.610359375, 0.523875, 0.4430703125, 0.3737890625, 0.3218984375, 0.281484375, 0.2521484375, 0.2281796875, 0.215703125], "ofdma_ser": [0.86271875, 0.7971171875, 0.721953125, 0.647375, 0.570296875, 0.5056015625, 0.4520703125, 0.4084375, 0.3757890625, 0.349140625, 0.333375], "noma_ser": [0.528281, 0.432492, 0.366477, 0.328313, 0.306781, 0.296672, 0.292969, 0.294141, 0.295102, 0.296852, 0.296914], "sfdma_ser": [0.8625, 0.797195, 0.721039, 0.644836, 0.574086, 0.509406, 0.447148, 0.407555, 0.373586, 0.347141, 0.331094], "uwca_analytic_ser": [0.752492, 0.654609, 0.552492, 0.45675, 0.37557, 0.304664, 0.247727, 0.210984, 0.180625, 0.159773, 0.144844]}
simulation/results/trained_HIGH.json
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{"scenario": "HIGH", "decoder_only": true, "snr_db": [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0], "maml_ser": [0.7294140625, 0.620421875, 0.5041328125, 0.3978671875, 0.3039609375, 0.230515625, 0.1738125, 0.131609375, 0.1019140625, 0.07890625, 0.062984375], "joint_ser": [0.7440703125, 0.6318203125, 0.518984375, 0.4146171875, 0.3212265625, 0.2481484375, 0.1878359375, 0.1431875, 0.113515625, 0.0883671875, 0.0736640625], "ofdma_ser": [0.8620546875, 0.795828125, 0.7221484375, 0.6450703125, 0.5720234375, 0.5067109375, 0.45159375, 0.4088515625, 0.374890625, 0.3501796875, 0.3289453125], "noma_ser": [0.396688, 0.269008, 0.195734, 0.170727, 0.172039, 0.182203, 0.192117, 0.200352, 0.205391, 0.209641, 0.211812], "sfdma_ser": [0.862758, 0.795562, 0.720273, 0.643914, 0.570617, 0.507945, 0.448648, 0.408813, 0.373148, 0.345977, 0.330164], "uwca_analytic_ser": [0.679406, 0.559523, 0.445, 0.339141, 0.253688, 0.187391, 0.138992, 0.104234, 0.078172, 0.061789, 0.049016]}
simulation/results/trained_LOW.json
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{"scenario": "LOW", "decoder_only": true, "snr_db": [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0], "maml_ser": [0.868234375, 0.8059140625, 0.73390625, 0.65996875, 0.584765625, 0.5230859375, 0.47259375, 0.4270234375, 0.3926953125, 0.3715, 0.3499765625], "joint_ser": [0.8706328125, 0.8068671875, 0.7355703125, 0.660375, 0.589296875, 0.5262890625, 0.4742734375, 0.432671875, 0.3999453125, 0.3717109375, 0.3539375], "ofdma_ser": [0.8624140625, 0.7946328125, 0.7270625, 0.6475703125, 0.5681484375, 0.5071640625, 0.4528046875, 0.4087265625, 0.3769375, 0.346203125, 0.326453125], "noma_ser": [0.772039, 0.712836, 0.668141, 0.637125, 0.618664, 0.609078, 0.60057, 0.594445, 0.593789, 0.591227, 0.590031], "sfdma_ser": [0.862852, 0.798078, 0.721359, 0.646039, 0.572836, 0.50975, 0.453109, 0.409164, 0.374375, 0.349133, 0.326977], "uwca_analytic_ser": [0.862031, 0.799906, 0.722148, 0.647344, 0.576445, 0.508508, 0.454688, 0.409336, 0.375422, 0.348648, 0.326375]}
simulation/results/trained_MIX.json
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{"scenario": "MIX", "decoder_only": true, "snr_db": [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0], "maml_ser": [0.8401796875, 0.7662109375, 0.6787578125, 0.5941796875, 0.5164296875, 0.448390625, 0.393078125, 0.343859375, 0.3110703125, 0.28565625, 0.268765625], "joint_ser": [0.841890625, 0.7675, 0.6803359375, 0.6049375, 0.527421875, 0.456296875, 0.40128125, 0.358921875, 0.31815625, 0.29290625, 0.2785234375], "ofdma_ser": [0.8638046875, 0.7965078125, 0.7235078125, 0.64375, 0.571640625, 0.5068125, 0.45346875, 0.4100703125, 0.3718203125, 0.3482734375, 0.3305859375], "noma_ser": [0.677984, 0.602898, 0.549969, 0.509781, 0.488984, 0.474641, 0.465477, 0.461117, 0.456508, 0.456258, 0.454094], "sfdma_ser": [0.861563, 0.796359, 0.722531, 0.64293, 0.574117, 0.50693, 0.453609, 0.408961, 0.374633, 0.345164, 0.326906], "uwca_analytic_ser": [0.824906, 0.747672, 0.660555, 0.571805, 0.497094, 0.425664, 0.370844, 0.327406, 0.290883, 0.262836, 0.244664]}
simulation/semantic_correlation_sim.py
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