Code and stored results for the IEEE Communications Letters submission

Semantic Multiplexing Gain in Wireless Systems via Expanded Embeddings:
A BERT Case Study. Includes the shared library, all experiment scripts
(training with and without SNR-aware MAML, the token-domain comparison,
the K sweep, and DistilBERT), the replot script that regenerates every
figure from the stored results, the supplementary probe-versus-cosine
analysis, and the raw results behind every figure in the letter.

probe_vs_cosine.py

@@ -0,0 +1,92 @@
+# probe_vs_cosine.py - supplementary analysis for the letter.
+#
+# Scatter of downstream probe accuracy (AG News topic classification,
+# linear probe trained on clean training-pool embeddings) against the
+# cosine similarity of the recovered embeddings, across schemes and
+# SNRs, with the Pearson correlation. Shows that the cosine metric used
+# in the letter is consistent with downstream perception.
+import json
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1.2})
+
+with open("fig_cl/cl_results.json") as f:
+    RJ = json.load(f)
+with open("fig_cl/cl_results_maml.json") as f:
+    RM = json.load(f)
+
+SNRS = ["0", "5", "10", "15", "20", "25", "30"]
+
+# (label, cos-source, acc-source, marker, color)
+def sweep_pairs(entry, todma=False):
+    cos, acc = [], []
+    for s in SNRS:
+        a = entry.get("probe_acc", {}).get(s)
+        if a is None:
+            continue
+        c = entry[s]["cos"] if todma else entry["snr"][s]["cos"]
+        cos.append(c)
+        acc.append(a)
+    return cos, acc
+
+
+SCHEMES = [
+    ("Proposed (MAML)", RM["mamlP_U4_K4"], False, "v", "#d62728"),
+    ("Training w/o MAML [5]", RJ["prop_U4_K4"], False, "x", "#8c564b"),
+    ("Random-projection mask (MAML)", RM["mamlR_U4_K4"], False, "s",
+     "#984ea3"),
+    ("Conventional orthogonal (MAML)", RM["mamlB_U1_K1"], False, "o",
+     "#1a1a1a"),
+    ("Conventional orthogonal (joint)", RJ["baseline_U1_K1"], False, "P",
+     "#7f7f7f"),
+    ("Random-projection mask (joint)", RJ["randmask_U4_K4"], False, "D",
+     "#c994c7"),
+    ("ToDMA 24x128", RJ["todma_T24_L128"], True, "^", "#4393c3"),
+]
+
+all_cos, all_acc = [], []
+fig = plt.figure(figsize=(7.0, 5.4))
+ax = fig.add_axes([0.12, 0.12, 0.83, 0.83])
+for lab, entry, todma, mk, col in SCHEMES:
+    cos, acc = sweep_pairs(entry, todma)
+    ax.scatter(cos, acc, marker=mk, s=70, color=col, label=lab,
+               zorder=3, alpha=0.9)
+    all_cos += cos
+    all_acc += acc
+
+all_cos = np.array(all_cos)
+all_acc = np.array(all_acc)
+r = np.corrcoef(all_cos, all_acc)[0, 1]
+b, a = np.polyfit(all_cos, all_acc, 1)
+xg = np.linspace(all_cos.min(), all_cos.max(), 50)
+ax.plot(xg, b * xg + a, color="#888888", linewidth=1.5, linestyle="--",
+        zorder=2, label=f"Linear fit (Pearson $r$={r:.3f})")
+clean = RM.get("probe_clean_acc", RJ.get("probe_clean_acc"))
+ax.axhline(clean, color="#bbbbbb", linewidth=1.2, linestyle=":",
+           zorder=1)
+ax.text(all_cos.min(), clean + 0.004,
+        f"Clean-embedding reference ({clean:.3f})", fontsize=11,
+        color="#888888")
+ax.set_xlabel("Cosine similarity of recovered embeddings", fontsize=15)
+ax.set_ylabel("Downstream probe accuracy", fontsize=15)
+ax.grid(True, alpha=0.3)
+ax.legend(fontsize=10.5, loc="lower right")
+fig.savefig("fig_cl/probe_vs_cosine.png", dpi=150)
+fig.savefig("fig_cl/probe_vs_cosine.pdf", dpi=200)
+print(f"Saved probe_vs_cosine.(png|pdf)  Pearson r = {r:.4f} "
+      f"over {len(all_cos)} scheme-SNR points")
+
+# Markdown table for the repository README
+print("\n| Scheme | CosSim 5 dB | Acc 5 dB | CosSim 20 dB | Acc 20 dB |")
+print("|---|---|---|---|---|")
+for lab, entry, todma, _, _ in SCHEMES:
+    def get(s):
+        c = entry[s]["cos"] if todma else entry["snr"][s]["cos"]
+        return c, entry.get("probe_acc", {}).get(s, float("nan"))
+    c5, a5 = get("5")
+    c20, a20 = get("20")
+    print(f"| {lab} | {c5:.3f} | {a5:.3f} | {c20:.3f} | {a20:.3f} |")
+
+