EDMA reproducibility package: simulation code, result data, and manuscript figures

2026-08-13 21:13:27 +09:00
commit b8b853e62e
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+"""Canonical replot of fig_sic.pdf from data/sic_comparison.csv
+(realizable analog SIC vs genie SIC vs EDMA vs OMA, beta = 0.311,
+d = 512, block-Rayleigh). US-spelling labels, uniform geometry."""
+import csv
+from pathlib import Path
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+ROOT = Path(__file__).resolve().parents[1]
+plt.rcParams.update({
+    "font.family": "serif",
+    "font.serif": ["DejaVu Serif", "Times New Roman"],
+    "font.size": 9, "axes.labelsize": 9, "legend.fontsize": 6.6,
+    "xtick.labelsize": 8, "ytick.labelsize": 8,
+    "axes.grid": True, "grid.linestyle": "--", "grid.linewidth": 0.4,
+    "grid.alpha": 0.6, "lines.linewidth": 1.4, "lines.markersize": 4.0,
+    "figure.figsize": (3.15, 2.36), "pdf.fonttype": 42,
+})
+AXES_RECT = dict(left=0.205, right=0.965, top=0.955, bottom=0.185)
+
+rows = list(csv.DictReader(open(ROOT / "data" / "sic_comparison.csv")))
+snr = [float(r["snr_db"]) for r in rows]
+col = lambda k: [float(r[k]) for r in rows]
+
+fig, ax = plt.subplots()
+ax.plot(snr, col("edma"), "o-", color="C3", label="EDMA (closed form)")
+ax.plot(snr, col("sic"), "^-.", color="C2", label="Realizable analog SIC")
+ax.plot(snr, col("oma"), "v:", color="C1", label="OMA")
+ax.plot(snr, col("genie"), "-", color="gray", lw=1.0,
+        label="Genie-aided SIC bound")
+ax.set_xlabel("Per-block SNR $\\rho$ [dB]")
+ax.set_ylabel("Mean cosine similarity")
+ax.set_xlim(snr[0], snr[-1])
+ax.set_ylim(0, 0.7)
+ax.legend(loc="upper left")
+fig.subplots_adjust(**AXES_RECT)
+fig.savefig(ROOT / "fig" / "fig_sic.pdf")
+print("[OK] wrote fig_sic.pdf")
+for r in rows:
+    print(f"  {float(r['snr_db']):4.0f} dB  EDMA {float(r['edma']):.3f}  "
+          f"SIC {float(r['sic']):.3f}  genie {float(r['genie']):.3f}  "
+          f"OMA {float(r['oma']):.3f}")