Simulation code and data for the TMC submission

code/exp1_theory.py

@@ -0,0 +1,107 @@
+"""E1: receiver theory verification (synthetic isotropic contents).
+
+Outputs: data/e1_beta.csv, data/e1_snr.csv, data/e1_endpoints.txt
+(figures come from replot_all.py only)
+"""
+import math
+import os
+import numpy as np
+from semantic_mac import (affinity_matrix, matched_filter, demux_sr, demux_sc,
+                          demux_lmmse, demux_dr, lmmse_matrices,
+                          sample_latents_isotropic, metrics,
+                          oma_observe, demux_noma_genie)
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+FIG = os.path.join(HERE, "..", "fig")
+DATA = os.path.join(HERE, "..", "data")
+os.makedirs(FIG, exist_ok=True)
+os.makedirs(DATA, exist_ok=True)
+
+U, D, DC = 4, 64, 16
+BATCH = 4000
+NAMES = ["OMA", "NOMA", "SR", "SC", "LMMSE", "DR"]
+
+
+def run_point(beta, rho, rng, conv_seed=0):
+    a = math.sqrt(beta) * np.ones(U)
+    B = affinity_matrix(a)
+    z = sample_latents_isotropic(BATCH, U, D, DC, a, rng)
+    tilde, h = matched_filter(z, B, rho, rng)
+    Vc = np.eye(D)[:, :DC]
+    res = {}
+    res["SR"] = metrics(demux_sr(tilde, B, h), z)
+    res["SC"] = metrics(demux_sc(tilde, h), z)
+    lm, cf = demux_lmmse(tilde, B, h, rho)
+    res["LMMSE"] = metrics(lm, z)
+    res["LMMSE_cf"] = float(cf.mean())
+    # SR closed form: d sigma^2 [B^-1]_uu / h^2 averaged
+    Binv = np.diag(np.linalg.inv(B))
+    res["SR_cf"] = float((D / rho) * (Binv[None, :] / h ** 2).mean())
+    res["DR"] = metrics(demux_dr(tilde, B, h, rho, a, Vc), z)
+    # conventional baselines on a dedicated stream (keeps main draws intact)
+    rng_c = np.random.default_rng(90000 + conv_seed)
+    res["OMA"] = metrics(oma_observe(z, h, rho, rng_c), z)
+    res["NOMA"] = metrics(demux_noma_genie(z, h, rho, rng_c), z)
+    return res
+
+
+def main():
+    rng = np.random.default_rng(0)
+    rho_db = 10
+    rho = 10 ** (rho_db / 10)
+    betas = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99]
+    rows = []
+    for b in betas:
+        r = run_point(b, rho, rng, conv_seed=int(b * 100))
+        rows.append(r)
+        print(f"beta={b:4.2f} " + " ".join(
+            f"{n}:cos={r[n][0]:.3f},nmse={r[n][1]:.3f},ser={r[n][2]:.3f}"
+            for n in NAMES))
+    with open(os.path.join(DATA, "e1_beta.csv"), "w") as f:
+        f.write("beta," + ",".join(f"{n}_cos,{n}_nmse,{n}_ser" for n in NAMES)
+                + ",LMMSE_cf,SR_cf\n")
+        for b, r in zip(betas, rows):
+            f.write(f"{b}," + ",".join(
+                f"{r[n][0]},{r[n][1]},{r[n][2]}" for n in NAMES)
+                + f",{r['LMMSE_cf']},{r['SR_cf']}\n")
+
+    beta_mid = 0.4
+    snrs = list(range(0, 21, 4))
+    rows_s = []
+    for s in snrs:
+        r = run_point(beta_mid, 10 ** (s / 10), rng, conv_seed=1000 + s)
+        rows_s.append(r)
+        print(f"snr={s} " + " ".join(f"{n}:ser={r[n][2]:.3f}" for n in NAMES))
+    with open(os.path.join(DATA, "e1_snr.csv"), "w") as f:
+        f.write("snr," + ",".join(f"{n}_cos,{n}_nmse,{n}_ser" for n in NAMES) + "\n")
+        for s, r in zip(snrs, rows_s):
+            f.write(f"{s}," + ",".join(
+                f"{r[n][0]},{r[n][1]},{r[n][2]}" for n in NAMES) + "\n")
+
+    # endpoint checks
+    lines = []
+    a = math.sqrt(0.4) * np.ones(U)
+    B = affinity_matrix(a)
+    h = np.clip(np.abs((rng.standard_normal(U) + 1j * rng.standard_normal(U))
+                       / math.sqrt(2)), 0.2, None)
+    for rdb in (10, 40, 80):
+        W, _ = lmmse_matrices(B, h, 10 ** (-rdb / 10), D)
+        Ginv = np.linalg.inv(np.diag(1 / h) @ B @ np.diag(h))
+        rel = np.linalg.norm(W - Ginv) / np.linalg.norm(Ginv)
+        lines.append(f"(i) rho={rdb}dB rel_diff_W_vs_Gammainv={rel:.3e}")
+    W0, _ = lmmse_matrices(np.eye(U), h, 0.1, D)
+    off = np.abs(W0 - np.diag(np.diag(W0))).max()
+    wiener = h ** 2 / (h ** 2 + D * 0.1)
+    lines.append(f"(ii) beta=0 max_offdiag={off:.3e} "
+                 f"max_diag_minus_wiener={np.abs(np.diag(W0)-wiener).max():.3e}")
+    with open(os.path.join(DATA, "e1_endpoints.txt"), "w") as f:
+        f.write("\n".join(lines) + "\n")
+    print("\n".join(lines))
+
+    # figures are produced only by the canonical replot_all.py (uniform
+    # geometry); experiment scripts write CSVs exclusively.
+    print("E1 done. Run replot_all.py to regenerate the figures.")
+
+
+if __name__ == "__main__":
+    main()