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Full-audit fixes: SIC naming, affinity-stat artifacts, gate diagnostics, figure legibility

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KiHoLee
2026-08-17 13:30:06 +09:00
parent 5b51f8b41a
commit 37114b11d0
13 changed files with 178 additions and 23 deletions
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code/refine_matched.py
+8 -1
View File
@@ -127,7 +127,14 @@ def train_refiners(epochs=220, steps=20, batch=48, lr=5e-4,
print(f" epoch {ep+1}: loss {float(loss.detach()):.4f} "
f"(cos {float(cs.detach()):.3f})", flush=True)
print(f" trained in {time.time()-t0:.0f}s")
return P_single, [p.detach() for p in params]
P4 = [p.detach() for p in params]
rel = float((P_single - sum(P4) / 4).norm() / P_single.norm())
print(f" [diag] ||P1 - mean(P4)|| / ||P1|| = {rel:.3e}")
np.savez(DATA / "refine_gates.npz",
P1=P_single.cpu().numpy(),
**{f"P4_{i}": p.cpu().numpy() for i, p in enumerate(P4)})
print(" [diag] gates saved to data/refine_gates.npz")
return P_single, P4
def refine_apply(ps, z):
code/replot_all.py
+25 -12
View File
@@ -57,6 +57,7 @@ def save(fig, name):
# ------------------------------------------------------ fig_floor
def fig_floor():
from matplotlib.lines import Line2D
rows = rows_of("floor_validation")
fig, ax = plt.subplots()
colors = {"256": "C0", "768": "C3"}
@@ -66,12 +67,10 @@ def fig_floor():
or float(r["d"]) == float(d)]
snr = col(rd, "snr_db")
ax.plot(snr, col(rd, "mse_mc"), "o", ms=3.5, color=colors[d],
mfc="none", label=rf"Monte Carlo, $d={d}$")
ax.plot(snr, col(rd, "mse_theory"), "-", color=colors[d],
label=rf"Theorem 1, $d={d}$")
mfc="none")
ax.plot(snr, col(rd, "mse_theory"), "-", color=colors[d])
if d == "768":
ax.plot(snr, col(rd, "mse_blind"), "--", color="C1", lw=1.2,
label=LBL["blind"])
ax.plot(snr, col(rd, "mse_blind"), "--", color="C1", lw=1.2)
g = 1.0 - beta**2
ax.axhline(math.sqrt(g) / 2, color="gray", lw=0.8, ls="--")
ax.axhline(0.5, color="gray", lw=0.8, ls=":")
@@ -81,12 +80,25 @@ def fig_floor():
fontsize=7, color="gray")
ax.set_xlabel("Per-block SNR $\\rho$ [dB]")
ax.set_ylabel(r"Per-user MSE $\mathbb{E}\|\hat{\mathbf{e}}_u-\mathbf{e}_u\|_2^2$")
ax.set_xlim(0, 40); ax.set_ylim(0.4, 1.32)
ax.set_yticks([0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
# legend in dedicated headroom above the curves (max 1.0), no overlap
ax.legend(loc="upper center", ncol=2, columnspacing=0.9,
handlelength=1.6, borderaxespad=0.3)
save(fig, "fig_floor")
ax.set_xlim(0, 40); ax.set_ylim(0.4, 1.05)
# one-row legend fully OUTSIDE the axes, flush to the top-right:
# composite handles (marker = Monte Carlo, line = Theorem 1; the
# convention is stated in the caption), so three entries fit one row
handles = [
Line2D([], [], color="C0", marker="o", mfc="none", ms=3.5,
ls="-", label="$d=256$"),
Line2D([], [], color="C3", marker="o", mfc="none", ms=3.5,
ls="-", label="$d=768$"),
Line2D([], [], color="C1", ls="--", lw=1.2, label=LBL["blind"]),
]
ax.legend(handles=handles, loc="lower right",
bbox_to_anchor=(1.0, 1.0), ncol=3, frameon=False,
columnspacing=1.0, handlelength=1.8, borderaxespad=0.0,
handletextpad=0.5)
fig.subplots_adjust(left=0.205, right=0.965, top=0.90, bottom=0.185)
fig.savefig(FIG / "fig_floor.pdf")
plt.close(fig)
print("[OK] wrote fig_floor.pdf")
# ------------------------------------------------ fig_rate_corrected
@@ -124,7 +136,7 @@ def fig_beta_sweep():
ax.plot([], [], ls="--", color="gray", label=LBL["oma"])
ax.plot([], [], ls="-.", color="gray", label=LBL["genie"])
for b0 in (0.030, 0.311):
ax.axvline(b0, color="gray", ls=":", lw=0.9)
ax.axvline(b0, ymax=0.62, color="gray", ls=":", lw=0.9)
ax.set_xlabel(r"Pairwise affinity $\beta$")
ax.set_ylabel("Effective sum rate [bps/Hz]")
ax.set_xlim(0, 1); ax.set_ylim(0, 1.0)
@@ -168,6 +180,7 @@ def fig_multiuser():
ax.set_xlabel("Per-block SNR $\\rho$ [dB]")
ax.set_ylabel("Effective sum rate [bps/Hz]")
ax.set_xlim(0, 30)
ax.set_ylim(bottom=0)
ax.legend(loc="upper left")
save(fig, "fig_multiuser_corrected")
code/replot_merged.py
+7 -6
View File
@@ -7,6 +7,7 @@ from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from replot_all import LBL
ROOT = Path(__file__).resolve().parents[1]
plt.rcParams.update({
@@ -25,17 +26,17 @@ 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")
ax.plot(snr, col("edma"), "o-", color="C3", label=LBL["edma"])
ax.plot(snr, col("edma_ref"), "^-", color="C2",
label="EDMA + refinement stage")
ax.plot(snr, col("todma"), "d-.", color="C4", label="ToDMA-adapted")
ax.plot(snr, col("oma"), "v:", color="C1", label="OMA")
label=LBL["hybrid"])
ax.plot(snr, col("todma"), "d-.", color="C4", label=LBL["todma"])
ax.plot(snr, col("oma"), "v:", color="C1", label=LBL["oma"])
ax.plot(snr, col("genie"), "-", color="gray", lw=1.0,
label="Genie-aided SIC bound")
label=LBL["genie"])
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, 0.85)
ax.set_ylim(0, 0.75)
ax.legend(loc="upper left")
fig.subplots_adjust(**AXES_RECT)
fig.savefig(ROOT / "fig" / "fig_bertvit_merged.pdf")