KiHoLee
358faecc0c
Redesign after the independent-mask dominance finding: the affinity now parameterizes the receiver (closed-form Wiener) instead of the mask ensemble. New Theorem 1 (spectral closed form), floors sqrt(1-b^2)/2 vs 1/2, full-cooperation bound with equality at b=1. GPU (torch) Monte Carlo backend, decision-directed SIC baseline, TikZ block diagram source, verification suite V1-V11.
180 lines
6.8 KiB
Python
180 lines
6.8 KiB
Python
"""Canonical figure rendering. Reads ONLY data/*.csv, writes fig/*.pdf.
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Figures: fig_floor, fig_rate_corrected, fig_beta_sweep_corrected,
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fig_sic, fig_multiuser_corrected. (fig_bertvit_merged is rendered by
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replot_merged.py; block_diagram.pdf comes from block_diagram_src.tex.)
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One physical geometry and one label dictionary for every plot.
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"""
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import csv
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import math
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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ROOT = Path(__file__).resolve().parents[1]
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DATA = ROOT / "data"
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FIG = ROOT / "fig"
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plt.rcParams.update({
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"font.family": "serif",
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"font.serif": ["DejaVu Serif", "Times New Roman"],
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"font.size": 9, "axes.labelsize": 9, "legend.fontsize": 6.6,
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"xtick.labelsize": 8, "ytick.labelsize": 8,
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"axes.grid": True, "grid.linestyle": "--", "grid.linewidth": 0.4,
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"grid.alpha": 0.6, "lines.linewidth": 1.4, "lines.markersize": 4.0,
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"figure.figsize": (3.15, 2.36), "pdf.fonttype": 42,
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})
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AXES_RECT = dict(left=0.205, right=0.965, top=0.955, bottom=0.185)
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LBL = {
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"edma": "EDMA",
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"blind": "Affinity-blind",
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"oma": "OMA",
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"genie": "Genie-aided SIC bound",
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"sic": "Realizable analog SIC",
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"todma": "ToDMA-adapted",
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"mac": "MAC sum capacity",
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"coop": "Full-cooperation bound",
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"hybrid": "EDMA + refinement stage",
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}
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def rows_of(name):
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return list(csv.DictReader(open(DATA / f"{name}.csv")))
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def col(rows, k):
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return [float(r[k]) for r in rows]
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def save(fig, name):
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fig.subplots_adjust(**AXES_RECT)
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fig.savefig(FIG / f"{name}.pdf")
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plt.close(fig)
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print(f"[OK] wrote {name}.pdf")
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# ------------------------------------------------------ fig_floor
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def fig_floor():
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rows = rows_of("floor_validation")
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fig, ax = plt.subplots()
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colors = {"256": "C0", "768": "C3"}
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beta = 0.311
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for d in ("256", "768"):
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rd = [r for r in rows if r["d"] == d or r["d"] == f"{d}.0"
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or float(r["d"]) == float(d)]
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snr = col(rd, "snr_db")
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ax.plot(snr, col(rd, "mse_mc"), "o", ms=3.5, color=colors[d],
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mfc="none", label=rf"Monte Carlo, $d={d}$")
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ax.plot(snr, col(rd, "mse_theory"), "-", color=colors[d],
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label=rf"Theorem 1, $d={d}$")
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if d == "768":
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ax.plot(snr, col(rd, "mse_blind"), "--", color="C1", lw=1.2,
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label=LBL["blind"])
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g = 1.0 - beta**2
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ax.axhline(math.sqrt(g) / 2, color="gray", lw=0.8, ls="--")
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ax.axhline(0.5, color="gray", lw=0.8, ls=":")
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ax.annotate("blind floor $1/2$", xy=(17.0, 0.512), fontsize=7,
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color="gray")
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ax.annotate(r"aware floor $\sqrt{1-\beta^2}/2$", xy=(14.0, 0.432),
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fontsize=7, color="gray")
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ax.set_xlabel("Per-block SNR $\\rho$ [dB]")
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ax.set_ylabel(r"Per-user MSE $\mathbb{E}\|\hat{\mathbf{e}}_u-\mathbf{e}_u\|_2^2$")
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ax.set_xlim(0, 40); ax.set_ylim(0.4, 1.05)
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ax.legend(loc="lower left", bbox_to_anchor=(0.02, 0.18))
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save(fig, "fig_floor")
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# ------------------------------------------------ fig_rate_corrected
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def fig_rate():
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rows = rows_of("rate_corrected")
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snr = col(rows, "snr_db")
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fig, ax = plt.subplots()
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ax.plot(snr, col(rows, "edma"), "-", color="C3", label=LBL["edma"])
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ax.plot(snr, col(rows, "blind"), ":", color="C4", lw=1.2,
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label=LBL["blind"])
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ax.plot(snr, col(rows, "oma"), "--", color="C1", label=LBL["oma"])
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ax.plot(snr, col(rows, "genie"), "-.", color="C0", label=LBL["genie"])
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ax.plot(snr, col(rows, "mac"), "-", color="k", lw=1.0, label=LBL["mac"])
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ax.set_xlabel("Per-block SNR $\\rho$ [dB]")
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ax.set_ylabel("Effective sum rate [bps/Hz]")
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ax.set_xlim(0, 40); ax.set_ylim(0, 3.2)
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ax.legend(loc="upper left")
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save(fig, "fig_rate_corrected")
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# ------------------------------------------ fig_beta_sweep_corrected
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def fig_beta_sweep():
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rows = rows_of("beta_sweep_corrected")
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fig, ax = plt.subplots()
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for s, cc in (("10", "C0"), ("20", "C3")):
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rd = [r for r in rows if float(r["snr_db"]) == float(s)]
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b = col(rd, "beta")
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ax.plot(b, col(rd, "edma"), "-", color=cc,
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label=rf"EDMA, $\rho={s}$ dB")
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ax.axhline(float(rd[0]["blind"]), color=cc, ls=":", lw=1.0)
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ax.axhline(float(rd[0]["oma"]), color=cc, ls="--", lw=1.0)
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ax.axhline(float(rd[0]["genie"]), color=cc, ls="-.", lw=0.8)
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# one legend entry per reference style (color-independent)
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ax.plot([], [], ls=":", color="gray", label=LBL["blind"])
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ax.plot([], [], ls="--", color="gray", label=LBL["oma"])
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ax.plot([], [], ls="-.", color="gray", label=LBL["genie"])
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for b0 in (0.030, 0.311):
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ax.axvline(b0, color="gray", ls=":", lw=0.9)
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ax.set_xlabel(r"Pairwise affinity $\beta$")
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ax.set_ylabel("Effective sum rate [bps/Hz]")
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ax.set_xlim(0, 1); ax.set_ylim(0, 1.0)
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ax.legend(loc="upper left")
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save(fig, "fig_beta_sweep_corrected")
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# ------------------------------------------------------- fig_sic
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def fig_sic():
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rows = rows_of("sic_comparison")
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snr = col(rows, "snr_db")
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fig, ax = plt.subplots()
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ax.plot(snr, col(rows, "edma"), "o-", color="C3", label=LBL["edma"])
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ax.plot(snr, col(rows, "blind"), "d:", color="C4", label=LBL["blind"])
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ax.plot(snr, col(rows, "sic"), "^-.", color="C2", label=LBL["sic"])
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ax.plot(snr, col(rows, "oma"), "v--", color="C1", label=LBL["oma"])
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ax.plot(snr, col(rows, "genie"), "-", color="gray", lw=1.0,
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label=LBL["genie"])
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ax.set_xlabel("Per-block SNR $\\rho$ [dB]")
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ax.set_ylabel("Mean cosine similarity")
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ax.set_xlim(snr[0], snr[-1]); ax.set_ylim(0, 0.7)
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ax.legend(loc="upper left")
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save(fig, "fig_sic")
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# ------------------------------------------ fig_multiuser_corrected
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def fig_multiuser():
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rows = rows_of("multiuser_corrected")
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fig, ax = plt.subplots()
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colors = {"2": "C0", "3": "C2", "4": "C3"}
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for U in ("2", "3", "4"):
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rd = [r for r in rows if float(r["U"]) == float(U)]
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snr = col(rd, "snr_db")
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ax.plot(snr, col(rd, "edma_mc"), "-", color=colors[U],
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label=rf"EDMA, $U={U}$")
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ax.plot(snr, col(rd, "oma"), "--", color=colors[U], lw=1.0,
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label=rf"OMA, $U={U}$")
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mk = [i for i, s in enumerate(snr) if s % 5 == 0]
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ax.plot([snr[i] for i in mk], [col(rd, "edma_mc")[i] for i in mk],
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"o", color=colors[U], ms=4, mfc="none")
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ax.set_xlabel("Per-block SNR $\\rho$ [dB]")
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ax.set_ylabel("Effective sum rate [bps/Hz]")
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ax.set_xlim(0, 30)
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ax.legend(loc="upper left")
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save(fig, "fig_multiuser_corrected")
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if __name__ == "__main__":
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import sys
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todo = set(sys.argv[1:])
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ALL = {"floor": fig_floor, "rate": fig_rate, "beta": fig_beta_sweep,
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"sic": fig_sic, "multi": fig_multiuser}
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for name, fn in ALL.items():
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if not todo or name in todo:
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fn()
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