From 5dff0a76a0b5e90e763cd3b89742bab5ef4945bf Mon Sep 17 00:00:00 2001 From: Ki-Ho Lee Date: Wed, 26 Aug 2026 22:19:24 +0900 Subject: [PATCH] Update the README for the current figures and results Fig. 3 is now the 10 dB aggregate with the token-domain bars, the non-MAML reference carries its figure label, the load-sweep script is listed, and the probe reference value matches the reported runs. --- README.md | 81 +++++++++++++++++++++++++++++++++++-------------------- 1 file changed, 52 insertions(+), 29 deletions(-) diff --git a/README.md b/README.md index a6f7195..8e99b57 100755 --- a/README.md +++ b/README.md @@ -1,27 +1,30 @@ -# Semantic Multiplexing Gain in Wireless Systems via Expanded Embeddings: A BERT Case Study +# 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 +`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. +demultiplexed by user-wise attention. Every transceiver reported in the +letter, including the conventional orthogonal scheme, is trained with +SNR-aware MAML; the same architecture trained without MAML, as in the +earlier JSAC paper, is included as a prior-art reference and is labelled +"Training w/o MAML [5]" in the figures. ## 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 | +| `cl_experiments.py` | Held-out split, configurations trained without MAML, token-domain (ToDMA) comparison, 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 expansion-factor sweep (K = 1, 2, 8) and DistilBERT replication | +| `cl_todma_load.py` | Evaluates the token-domain scheme at U = 1, 2, 3, 5, 6 for the load sweep of Fig. 3 | | `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 | +| `fig_cl/*.json`, `fig_cl/*.csv` | Stored raw results behind every figure and every number quoted in the letter | ## Reproducing @@ -30,52 +33,72 @@ Requirements: Python 3.10+, PyTorch (CUDA), `transformers`, `datasets`, (`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_experiments.py --save-dir fig_cl # runs without MAML + token-domain comparison (~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 cl_todma_load.py --save-dir fig_cl # token-domain load sweep (evaluation only) +python replot_cl.py # Figs. 2 and 3 from the 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. +seed 123, token-domain seed 7) and evaluate on a held-out test split of +2,000 AG News sentences disjoint from the 8,000-sentence training pool. +The centering mean, the transceiver parameters, and the linear probe are +fitted on the training pool only. `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 - per-user cosine similarity vs. SNR.** The proposed scheme for +U = 1..4 at K = 4, the conventional orthogonal scheme, the two +matched-budget comparison schemes (random-projection mask and the +token-domain scheme at two slot/codeword splits), and the same +architecture trained without MAML: ![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 - aggregate fidelity across load at 10 dB.** Three bars per +load point (proposed, training without MAML, and the token-domain +scheme at 24x128), the conventional single-user block on the left, and +the fully loaded orthogonal aggregate as the dash-dotted reference: ![Fig. 3](fig_cl/cl_fig_agg.png) +At full load the shared block stays within 1% of the fully loaded +orthogonal aggregate over the same 3072 channel uses (3.59 against +3.62), and under overload it reaches 1.45 times that aggregate at +U = 6. + ## 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). +metric, namely the AG News topic accuracy of a linear probe fitted on +the training pool and applied to the recovered test embeddings. The +noiseless reference is about 0.854 and the sampling error is 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 +Two readings follow. First, the low-SNR advantage of the embedding +schemes over the token-domain scheme appears in both metrics, for +example 0.848 against 0.772 in cosine similarity and 0.805 against +0.762 in accuracy at 5 dB. Second, schemes within about 0.01 of each +other in cosine similarity differ in accuracy by at most 0.023, which +is within about two standard errors of the sampling noise, so the cosine metric used throughout the letter is consistent with downstream perception on this task. +## Citation +The letter is under review. Until it appears, please cite this +repository together with the earlier shared-embedding paper: + +> K.-H. Lee, H.-H. Choi, and J.-R. Lee, "Transformer-based shared +> embedding for multiple access in semantic communications," *IEEE J. +> Sel. Areas Commun.*, vol. 44, pp. 2622-2637, 2026.