edma-semantic-mac/README.md

EDMA — Affinity-Aware Embedding Division Multiple Access

Reproducibility package for

K.-H. Lee, H.-H. Choi, and J.-R. Lee, "Affinity-Aware Embedding Division Multiple Access for Multi-User Semantic Communications," submitted to IEEE Transactions on Vehicular Technology, 2026.

This repository contains the simulation code, the raw result data, and the figure files behind every numerical claim in the paper. It is private during peer review and will be made public upon publication.

The design under test: each user applies an independent Haar orthogonal mask, and the receiver runs a matched filter followed by the closed-form affinity-aware Wiener demultiplexer, which harvests the coherent interference component that the measured pairwise affinity beta predicts. The affinity-blind reference sets beta = 0 in the same filter.

Layout

Folder	Contents
code/	Simulation and plotting scripts (Python)
data/	Raw results written by the scripts, one CSV per experiment
fig/	Figure PDFs included in the manuscript (block_diagram_src.tex is the TikZ source of Fig. 1)

Requirements

Python 3.10 or later with numpy and matplotlib. The Monte Carlo experiments in revision_sims_gpu.py, fig_real_merged.py, and refine_matched.py use torch (CUDA when available; the scripts fall back to CPU). All random draws come from the numpy generator with the fixed seed 2026 — torch only accelerates QR, matrix products, and linear solves — and every script writes its raw output to data/, so plotting is fully decoupled from simulation.

Reproducing the figures

Run the scripts from inside code/. All plots are rendered from data/ only, by replot_all.py (Figs. 2, 3, 5, 6, 7) and replot_merged.py (Fig. 4).

Figure	Content	Simulation	Data
Fig. 1	System diagram	latexmk -pdf fig/block_diagram_src.tex	—
Fig. 2	Per-user MSE, aware vs blind floor	revision_sims.py E1	floor_validation.csv
Fig. 3	Effective sum rate at the CLIP affinity	revision_sims.py E7a	rate_corrected.csv
Fig. 4	Cosine recovery on real BERT+ViT pairs	fig_real_merged.py, then refine_matched.py	bertvit_merged.csv
Fig. 5	Receiver comparison under Rayleigh fading	revision_sims_gpu.py E2	sic_comparison.csv
Fig. 6	Value of the measured affinity	revision_sims.py E7a	beta_sweep_corrected.csv
Fig. 7	Multi-user scaling (joint Wiener)	revision_sims_gpu.py E7c	multiuser_corrected.csv

Quantities quoted in the text but not plotted come from the same drivers: revision_sims.py E0 writes theorem_check.csv (Theorem 1 validation across affinities and channel phases), revision_sims_gpu.py E3 writes rayleigh_mse.csv (unconditional Rayleigh MSE), E4 writes csi_error.csv (imperfect-CSI robustness), E5 writes mask_family_rev.csv (Walsh–Hadamard versus Haar), E8 writes mismatch.csv (affinity mismatch and quantization), and E9 writes cosine_ceiling.csv (cosine-ceiling corollary check).

Verifying the analysis

verify_math.py re-derives every closed-form claim numerically and prints one PASS/FAIL line per item: Theorem 1 at the equal-gain point and under random channel phases for both users, the aware and blind error floors and the value-of-affinity ratio, the cosine-ceiling corollary, the blind-receiver/matched-filter cosine equivalence, the monotonicity proposition, the full-cooperation bound with its equality case at beta = 1, the finite-SNR MAC-condition boundary, the exact Walsh–Hadamard closed form, the quadratic mismatch stationarity, and the dominated floor of the correlated-mask alternative from the Appendix. It depends only on numpy.

Conventions

The scripts follow the manuscript exactly: unit per-block transmit energy E_b = 1 per user, rho = E_b / sigma_n^2 as the per-block SNR with per-symbol SNR rho/d, complex block-Rayleigh gains unless the evaluation point h_u = 1 is stated, real unit-norm embeddings with the orientation <e1, e2> = +beta, and independent Haar masks drawn fresh on every realization.

Citation and license

Citation details and a license will be added when the paper is published.