CEM Semantic Task Validation (Sec. III-E)

Code and evaluation script for the semantic task validation experiment (Sec. III-E) of the letter

K.-H. Lee, H.-H. Choi, and J.-R. Lee, "Contrastive Embedding Multiplexing for Multi-User Semantic Communication Systems," submitted to IEEE Signal Processing Letters (manuscript SPL-48226-2026).

Contrastive embedding multiplexing (CEM) multiplexes several users in one shared embedding space: a user-specific positional mask assigns each user a soft subspace, and an InfoNCE contrastive objective (trained jointly with the reconstruction loss) drives the channel-corrupted receiver-side representations of different users toward near-orthogonality. This repository verifies that the symbol-level gains carry over to a practical semantic task, namely text transmission scored by BLEU.

What the experiment does

cem_text.py transmits English sentences from the Europarl corpus with U = 4 users (letter configuration) through the CEM pipeline (user-specific masking -> shared Transformer encoder -> 1/U superposition -> Rayleigh fading + AWGN -> masked-query cross-attention decoding) and reports corpus-averaged sentence BLEU-4 (add-one smoothing on the higher n-gram precisions) on a held-out 5% test split.

• Vocabulary: the 22,000 most frequent lowercase words (+ PAD/UNK)
• Token length T = 32, embedding dimension d = 128, projection dimension 64, temperature 0.1
• Two configurations are trained under an identical protocol (8,000 steps, AdamW, per-batch SNR drawn uniformly from 0-25 dB): the CE scheme (mask only, lambda = 0) and the proposed CE + NCE scheme (lambda = 0.001, the operating value adopted in the letter)

How to run

1. Download the English side of the French-English Europarl v7 corpus from https://www.statmt.org/europarl/ and place it at data/europarl-v7.fr-en.en.

2. Run:

   python cem_text.py --users 4 --lam 0        # CE scheme (letter configuration)
   python cem_text.py --users 4 --lam 0.001    # proposed scheme (letter configuration)
   python cem_text.py                          # previous-configuration U = 8 pair (lambda = 0.01)
   

   Results are written to results_bleu.csv.

Expected results (single seed)

Scheme	BLEU @ 10 dB	BLEU @ 20 dB
Single-user reference (U = 1, CE)	0.994	0.995
CE (mask only, U = 4, d = 128)	0.183	0.184
CE + NCE (proposed, U = 4, d = 128, lambda = 1e-3, letter configuration)	0.369	0.394
CE (mask only, U = 8, d = 128, previous configuration)	0.117	0.118
CE + NCE (U = 8, d = 256, lambda = 1e-3)	0.456	0.491

The CE scheme's BLEU is flat in SNR, indicating an interference-limited regime; the contrastive term alleviates it, so embedding-level separation translates into semantic-level recovery (at the letter configuration U = 4 the proposed scheme roughly doubles the BLEU).

results/results_bleu.csv contains the numbers reported in the letter.

The single-user interference-free reference can be reproduced with python cem_text.py --users 1. Its near-perfect BLEU shows that the lower scores at U = 4 and U = 8 come from inter-user interference rather than from the text model itself.

The absolute BLEU is governed by the embedding capacity relative to the user load. Doubling the embedding dimension under the adopted weight (python cem_text.py --dim 256 --lam 0.001) raises the U = 8 BLEU from 0.117 to 0.456/0.491, confirming the capacity trend reported in the response letter.

A no-mask ablation (user-specific masking disabled, contrastive term only) can be reproduced with python cem_text.py --include-no-mask. At the symbol level this configuration fails entirely (SER pinned near 0.56 at all SNRs; Sec. III-D of the letter), showing that the mask and the contrastive term are complementary.

Requirements

• Python >= 3.10, PyTorch >= 2.0 (CUDA or Apple MPS optional; CPU works)

License

MIT (see LICENSE).