Ki-Ho Lee
248e637f55
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.
93 lines
3.4 KiB
Python
Executable File
93 lines
3.4 KiB
Python
Executable File
# probe_vs_cosine.py - supplementary analysis for the letter.
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#
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# Scatter of downstream probe accuracy (AG News topic classification,
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# linear probe trained on clean training-pool embeddings) against the
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# cosine similarity of the recovered embeddings, across schemes and
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# SNRs, with the Pearson correlation. Shows that the cosine metric used
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# in the letter is consistent with downstream perception.
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import json
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import numpy as np
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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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plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1.2})
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with open("fig_cl/cl_results.json") as f:
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RJ = json.load(f)
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with open("fig_cl/cl_results_maml.json") as f:
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RM = json.load(f)
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SNRS = ["0", "5", "10", "15", "20", "25", "30"]
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# (label, cos-source, acc-source, marker, color)
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def sweep_pairs(entry, todma=False):
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cos, acc = [], []
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for s in SNRS:
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a = entry.get("probe_acc", {}).get(s)
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if a is None:
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continue
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c = entry[s]["cos"] if todma else entry["snr"][s]["cos"]
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cos.append(c)
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acc.append(a)
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return cos, acc
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SCHEMES = [
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("Proposed (MAML)", RM["mamlP_U4_K4"], False, "v", "#d62728"),
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("Training w/o MAML [5]", RJ["prop_U4_K4"], False, "x", "#8c564b"),
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("Random-projection mask (MAML)", RM["mamlR_U4_K4"], False, "s",
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"#984ea3"),
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("Conventional orthogonal (MAML)", RM["mamlB_U1_K1"], False, "o",
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"#1a1a1a"),
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("Conventional orthogonal (joint)", RJ["baseline_U1_K1"], False, "P",
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"#7f7f7f"),
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("Random-projection mask (joint)", RJ["randmask_U4_K4"], False, "D",
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"#c994c7"),
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("ToDMA 24x128", RJ["todma_T24_L128"], True, "^", "#4393c3"),
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]
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all_cos, all_acc = [], []
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fig = plt.figure(figsize=(7.0, 5.4))
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ax = fig.add_axes([0.12, 0.12, 0.83, 0.83])
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for lab, entry, todma, mk, col in SCHEMES:
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cos, acc = sweep_pairs(entry, todma)
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ax.scatter(cos, acc, marker=mk, s=70, color=col, label=lab,
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zorder=3, alpha=0.9)
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all_cos += cos
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all_acc += acc
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all_cos = np.array(all_cos)
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all_acc = np.array(all_acc)
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r = np.corrcoef(all_cos, all_acc)[0, 1]
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b, a = np.polyfit(all_cos, all_acc, 1)
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xg = np.linspace(all_cos.min(), all_cos.max(), 50)
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ax.plot(xg, b * xg + a, color="#888888", linewidth=1.5, linestyle="--",
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zorder=2, label=f"Linear fit (Pearson $r$={r:.3f})")
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clean = RM.get("probe_clean_acc", RJ.get("probe_clean_acc"))
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ax.axhline(clean, color="#bbbbbb", linewidth=1.2, linestyle=":",
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zorder=1)
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ax.text(all_cos.min(), clean + 0.004,
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f"Clean-embedding reference ({clean:.3f})", fontsize=11,
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color="#888888")
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ax.set_xlabel("Cosine similarity of recovered embeddings", fontsize=15)
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ax.set_ylabel("Downstream probe accuracy", fontsize=15)
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ax.grid(True, alpha=0.3)
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ax.legend(fontsize=10.5, loc="lower right")
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fig.savefig("fig_cl/probe_vs_cosine.png", dpi=150)
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fig.savefig("fig_cl/probe_vs_cosine.pdf", dpi=200)
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print(f"Saved probe_vs_cosine.(png|pdf) Pearson r = {r:.4f} "
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f"over {len(all_cos)} scheme-SNR points")
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# Markdown table for the repository README
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print("\n| Scheme | CosSim 5 dB | Acc 5 dB | CosSim 20 dB | Acc 20 dB |")
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print("|---|---|---|---|---|")
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for lab, entry, todma, _, _ in SCHEMES:
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def get(s):
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c = entry[s]["cos"] if todma else entry["snr"][s]["cos"]
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return c, entry.get("probe_acc", {}).get(s, float("nan"))
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c5, a5 = get("5")
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c20, a20 = get("20")
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print(f"| {lab} | {c5:.3f} | {a5:.3f} | {c20:.3f} | {a20:.3f} |")
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