TMC/README.md

Structured Shared–Private Embedding Multiplexing

Simulation code and data for the manuscript

K.-H. Lee, H.-H. Choi, and J.-R. Lee, "Structured Shared–Private Embedding Multiplexing for Semantic Multiple Access in Dynamic Mobile Networks," submitted to IEEE Transactions on Mobile Computing, 2026.

Builds on the published shared-embedding multiple-access framework (Lee, Choi, Lee, IEEE JSAC, vol. 44, 2026, doi 10.1109/JSAC.2025.3643816).

Layout

• code/semantic_mac.py — core library (content model, matched-filter front end, SR/SC/LMMSE/DR receivers, spectral structure recovery, mobility model, affinity tracker)
• code/exp1_theory.py — E1: receiver theory validation (affinity and SNR sweeps, closed-form overlays) → data/e1_*.csv
• code/exp2_structure.py — E2: embedding-structure optimization on real BERT embeddings (spectral recovery, learned adapter, held-out evaluation ladder) → data/e2_*.csv, data/e2_exponents.txt
• code/exp3_mobility.py — E3: time-varying affinity tracking under mobility (scene traversal and speed sweep) → data/e3_*.csv
• code/exp4_mismatch.py — E4: robustness to affinity estimation error → data/e4_mismatch.csv
• code/exp5_learned.py — E5: comparison with a trained user-wise attention receiver → data/e5_learned.csv, data/e5_train_log.csv
• code/check_mask_realization.py — realized Haar-mask front end vs the expected cross-Gram model → data/e_mask_check.csv
• code/replot_all.py — the single canonical figure generator; reads only data/*.csv and writes every paper figure with a uniform canvas geometry
• code/lmmse_verify.py — early derivation-check prototype for the affinity-aware LMMSE proposition (predecessor of E1)
• data/bert_agnews_8000.pt — frozen bert-base-uncased mean-pooled embeddings of 8,000 AG News sentences (768-dim), the real-content pool used by E2

Reproduction

Requirements: Python 3.10+, numpy, torch (CPU is sufficient), matplotlib.

cd code
python exp1_theory.py      # E1  (minutes)
python exp2_structure.py   # E2  (about an hour on CPU)
python exp3_mobility.py    # E3  (about an hour on CPU)
python exp4_mismatch.py    # E4  (minutes)
python exp5_learned.py     # E5  (minutes)
python replot_all.py       # regenerate every figure from data/*.csv

Every experiment fixes its random seeds, experiment scripts write CSVs only, and replot_all.py is the only script that produces figures, so each figure in the paper is regenerable from the shipped CSVs without rerunning the experiments.

License

MIT — see LICENSE.