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.
Verify the appendix bound against its stated constant
2026-08-26 14:44:52 +09:00 · 2026-08-26 13:53:55 +09:00 · 2026-08-26 09:20:43 +09:00 · 2026-08-25 23:02:19 +09:00 · 2026-08-25 22:32:15 +09:00 · 2026-08-25 22:19:43 +09:00
36 changed files with 4885 additions and 2415 deletions
@@ -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`.

@@ -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` ->
@@ -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)")
@@ -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
@@ -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)
@@ -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}
@@ -96,9 +96,9 @@ SCENARIOS = {
 USER_COLORS = ['#1565C0', '#2E7D32', '#C62828', '#6A1B9A']

 MCFG = {
-    'OFDMA':      ('#546E7A', 's--', 1.5, 'OFDMA'),
+    'OFDMA':      ('#546E7A', 's--', 1.5, 'OMA'),
    'NOMA-SIC':   ('#E65100', '^-',  1.5, 'NOMA'),
-    'MAML+Attn':  ('#1565C0', 'o-',  2.4, 'UWCA (proposed)'),
+    'MAML+Attn':  ('#1565C0', 'o-',  2.4, 'UWCA (trained)'),
 }

 CMAP_RHO  = LinearSegmentedColormap.from_list('rho',  ['#1565C0', '#FFFFFF', '#C62828'], N=256)
@@ -425,7 +425,7 @@ def _overlay_trained(ax, scenario_key: str):
        return
    snr = tr['snr_db']
    ax.semilogy(snr, tr['maml_ser'], 'o', ms=7, mfc='none', mec='#1565C0',
-                mew=1.8, label='UWCA (Trained)', zorder=5)
+                mew=1.8, label='UWCA (trained)', zorder=5)


 def _snr_at_ser(ser_arr, snr_arr, target=0.30):
@@ -524,7 +524,7 @@ for sl, col, lbl in _groups:
                 'o-', lw=1.8, color=col, label=f'{lbl} — UWCA')
 # OFDMA: β-independent → single curve averaged over all users
 ax3.semilogy(SNR_DB, res['OFDMA']['sp'].mean(axis=1),
-             's--', lw=1.2, color='#546E7A', label='OFDMA (reference)')
+             's--', lw=1.2, color='#546E7A', label='OMA')
 # NOMA: power-allocation-dependent → single curve averaged over all users
 ax3.semilogy(SNR_DB, res['NOMA-SIC']['sp'].mean(axis=1),
             '^-', lw=1.2, color='#E65100', label='NOMA (reference)')
@@ -687,7 +687,7 @@ for U_val in _U_LIST_9:

 # Unified legend: OFDMA + UWCA-SE per U value
 legend_handles = [
-    _L2D([0],[0], color='#546E7A', lw=1.6, ls='--', alpha=0.75, label='OFDMA (Analytical)'),
+    _L2D([0],[0], color='#546E7A', lw=1.6, ls='--', alpha=0.75, label='OMA'),
    _L2D([0],[0], color=_U_COLORS[1], lw=2.2, ls='-', label='UWCA ($U=1$)'),
    _L2D([0],[0], color=_U_COLORS[2], lw=2.2, ls='-', label='UWCA ($U=2$)'),
    _L2D([0],[0], color=_U_COLORS[4], lw=2.2, ls='-', label='UWCA ($U=4$)'),
@@ -726,7 +726,7 @@ for U_val in MI_U_LIST:

 _style_handles = [
    Line2D([0], [0], color='k', lw=2.2, ls='-',  label='UWCA (Analytical)'),
-    Line2D([0], [0], color='k', lw=1.6, ls='--', alpha=0.6, label='OFDMA (Analytical)'),
+    Line2D([0], [0], color='k', lw=1.6, ls='--', alpha=0.6, label='OMA'),
 ]
 _color_handles = [Line2D([0],[0], color=_U_COLORS[u], lw=2.2, label=f'U={u}')
                  for u in MI_U_LIST]
@@ -848,7 +848,7 @@ ax11a.axhline(TAU_FAIR, color='gray', lw=0.8, ls=':', alpha=0.6)
 ax11a.text(0.5, TAU_FAIR * 1.07, f'$\\tau={TAU_FAIR}$', fontsize=8, color='gray')

 _lh_fair = [
-    _L2D([0],[0], color='k', lw=1.5, ls='--', alpha=0.6, label='OFDMA (Analytical)'),
+    _L2D([0],[0], color='k', lw=1.5, ls='--', alpha=0.6, label='OMA'),
    _L2D([0],[0], color='k', lw=2.2, ls='-',             label='UWCA (Analytical)'),
 ] + [
    _L2D([0],[0], color=_U_COLORS[u], lw=2.2, label=f'$U={u}$') for u in _U_LIST_F
@@ -937,7 +937,7 @@ def _draw_panel12(ax, u_results, beta_label, panel_tag):
    ax.grid(True, alpha=0.3); ax.set_xlim(0, 20)
    _handles = [
        _L2D([0],[0], color=_OFDMA_CLR12, lw=2.2, ls='-',
-             label='OFDMA'),
+             label='OMA'),
        _L2D([0],[0], color=_UWCA_CLRS12[1], lw=1.5, ls='-',
             marker=_UWCA_MKRS12[1], ms=4, label='UWCA ($U=1$)'),
        _L2D([0],[0], color=_UWCA_CLRS12[2], lw=1.5, ls='-',
@@ -101,13 +101,15 @@ for s, ca in SCEN.items():
 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}
+# 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='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]['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 (analytical)')
+    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)
@@ -121,6 +123,10 @@ 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} "
@@ -0,0 +1,9 @@
+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
@@ -0,0 +1,49 @@
+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
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@@ -0,0 +1,58 @@
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@@ -0,0 +1,67 @@
+snr_db,U,method,ser
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@@ -0,0 +1,801 @@
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@@ -0,0 +1,505 @@
+snr_db,beta,U,method,ser
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+15.0,0.5,1,OFDMA,0.008361979166666667
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+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
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+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
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+14.0,0.1,1,OFDMA,0.010658854166666667
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+15.0,0.1,1,OFDMA,0.008330729166666667
+15.0,0.1,1,UWCA,0.008330729166666667
+16.0,0.1,1,OFDMA,0.006502604166666667
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+17.0,0.1,1,OFDMA,0.005291666666666667
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+18.0,0.1,1,OFDMA,0.0040963541666666665
+18.0,0.1,1,UWCA,0.0040963541666666665
+19.0,0.1,1,OFDMA,0.003395833333333333
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+20.0,0.1,1,OFDMA,0.0026276041666666665
+20.0,0.1,1,UWCA,0.0026276041666666665
+0.0,0.1,2,OFDMA,0.49913671875
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+1.0,0.1,2,OFDMA,0.43283333333333335
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+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
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+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
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+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
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+11.0,0.1,2,OFDMA,0.06420833333333334
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+15.0,0.1,2,OFDMA,0.02662890625
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+19.0,0.1,2,OFDMA,0.010752604166666667
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+20.0,0.1,2,UWCA,0.00865234375
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+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
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+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
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+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
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+17.0,0.1,3,OFDMA,0.11201996527777773
+17.0,0.1,3,UWCA,0.11193315972222223
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+19.0,0.1,3,OFDMA,0.09420225694444455
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+20.0,0.1,3,OFDMA,0.08725520833333329
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+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
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+3.0,0.1,4,UWCA,0.760072265625
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+7.0,0.1,4,OFDMA,0.607173828125
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+8.0,0.1,4,OFDMA,0.5727662760416666
+8.0,0.1,4,UWCA,0.572451171875
+9.0,0.1,4,OFDMA,0.5384420572916667
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+10.0,0.1,4,OFDMA,0.507392578125
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+11.0,0.1,4,OFDMA,0.4781666666666667
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+12.0,0.1,4,OFDMA,0.452376953125
+12.0,0.1,4,UWCA,0.45219205729166667
+13.0,0.1,4,OFDMA,0.4290950520833333
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+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
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+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
@@ -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
@@ -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
@@ -0,0 +1,164 @@
+{
+ "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": [
+   0.959921875,
+   0.9588671875,
+   0.9374609375,
+   0.90783203125,
+   0.88005859375,
+   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,
+   0.52474609375,
+   0.5308984375
+  ],
+  "SFDMA": [
+   0.38158203125,
+   0.41474609375,
+   0.4375,
+   0.46376953125,
+   0.48546875,
+   0.49544921875,
+   0.5080859375,
+   0.5149609375,
+   0.52048828125,
+   0.5258984375,
+   0.53150390625
+  ],
+  "NOMA-SIC": [
+   0.18515625,
+   0.17927734375,
+   0.17955078125,
+   0.17869140625,
+   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
+  ]
+ }
+}
@@ -0,0 +1 @@
+{"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]}}
@@ -0,0 +1 @@
+{"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]}
@@ -0,0 +1 @@
+{"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]}
@@ -0,0 +1 @@
+{"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]}
@@ -0,0 +1 @@
+{"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]}
@@ -0,0 +1 @@
+{"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]}
@@ -176,7 +176,7 @@ USER_COLORS = ['#1565C0', '#2E7D32', '#C62828', '#6A1B9A']
 # Method display config:  color / marker+linestyle / linewidth / legend label
 MCFG = {
    'OFDMA':      ('#546E7A', 's--', 1.5, 'OFDMA (Analytical)'),
-    'MAML+Attn':  ('#1565C0', 'o-',  2.4, 'UWCA-SE (Analytical)'),
+    'MAML+Attn':  ('#1565C0', 'o-',  2.4, 'UWCA (analytical)'),
 }
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
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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]}}
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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]}
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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]}
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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]}
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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]}
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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]}