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.

README.md

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+# Semantic Multiplexing Gain in Wireless Systems via Expanded Embeddings: A BERT Case Study
+
+Code, stored results, and supplementary material for the IEEE
+Communications Letters submission by Ki-Ho Lee, Hyun-Ho Choi, and
+Jung-Ryun Lee.
+
+Multiple users share one expanded embedding block of dimension
+`d_s = K * d_b`: each user's frozen BERT sentence embedding is projected
+into the shared space, superimposed through learnable masks, and
+demultiplexed by user-wise attention. All reported transceivers are
+trained with SNR-aware MAML; the training without MAML of the earlier
+JSAC paper is included as a prior-art reference.
+
+## Files
+
+| File | Purpose |
+|---|---|
+| `bert_semcom.py` | Shared library: BERT extractor, transceiver model, channel, MAML helpers |
+| `cl_experiments.py` | Held-out split, joint-trained configurations, ToDMA token-domain benchmark, linear probe, latency |
+| `cl_maml_all.py` | SNR-aware MAML training for every reported configuration (including the conventional orthogonal scheme) |
+| `cl_maml_extra.py` | MAML K sweep (K = 1, 2, 8) and DistilBERT replication |
+| `replot_cl.py` | Regenerates Figs. 2 and 3 of the letter from the stored JSON results |
+| `probe_vs_cosine.py` | Supplementary probe-accuracy-versus-cosine-similarity analysis |
+| `fig_cl/*.json`, `fig_cl/*.csv` | Stored raw results behind every figure and quoted number |
+
+## Reproducing
+
+Requirements: Python 3.10+, PyTorch (CUDA), `transformers`, `datasets`,
+`matplotlib`, `numpy`. AG News loads from the Hugging Face hub
+(`fancyzhx/ag_news` fallback included).
+
+```bash
+python cl_experiments.py --save-dir fig_cl   # joint runs + ToDMA benchmark (~3 h on a laptop GPU)
+python cl_maml_all.py    --save-dir fig_cl   # MAML runs (~9 h)
+python cl_maml_extra.py  --save-dir fig_cl   # MAML K sweep + DistilBERT (~6 h)
+python replot_cl.py                          # Figs. 2 and 3 from stored results
+python probe_vs_cosine.py                    # supplementary analysis below
+```
+
+All experiments fix their random seeds (training seed 42, evaluation
+seed 123, ToDMA seed 7) and evaluate on a held-out test split of 2,000
+AG News sentences disjoint from the 8,000-sentence training pool.
+`replot_cl.py` and `probe_vs_cosine.py` read only the stored results,
+so every figure is regenerable without rerunning the experiments.
+
+## Figures of the letter
+
+**Fig. 2 - per-user cosine similarity vs. SNR** (proposed scheme for
+U = 1..4 at K = 4, the conventional orthogonal scheme, the
+matched-budget schemes, and the joint training of the earlier JSAC
+paper, all on the held-out test set):
+
+![Fig. 2](fig_cl/cl_fig_mux.png)
+
+**Fig. 3 - aggregate fidelity across load** (SNR-aware MAML vs. joint
+training at 20 dB, with the fully loaded orthogonal reference):
+
+![Fig. 3](fig_cl/cl_fig_agg.png)
+
+## Supplementary: probe accuracy vs. cosine similarity
+
+The letter measures semantic fidelity by the cosine similarity of the
+recovered embeddings and corroborates it with a downstream perception
+metric: the AG News topic accuracy of a linear probe trained on clean
+training-pool embeddings and applied to the recovered test embeddings
+(clean reference about 0.855, sampling error about +/-0.01).
+
+Across 7 schemes x 7 SNRs (49 operating points), probe accuracy tracks
+cosine similarity with a Pearson correlation of **r = 0.903**:
+
+![Probe accuracy vs. cosine similarity](fig_cl/probe_vs_cosine.png)
+
+Two readings follow. First, the low-SNR advantage of the analog
+embedding schemes over the token-domain scheme appears in both metrics
+(for example 0.848 vs. 0.772 in CosSim and 0.805 vs. 0.762 in accuracy
+at 5 dB). Second, schemes within about 0.01 of each other in CosSim
+differ in accuracy only on the order of the sampling error, so the
+cosine metric used throughout the letter is consistent with downstream
+perception on this task.
+
+