Simulation code, raw results, and figure sources for the manuscript "Relevance-Aware Semantic Multiple Access via Meta-Learned User-Wise Cross-Attention" (submitted to IEEE Transactions on Wireless Communications).

Requirements

Python 3.10+ with torch (CUDA optional; results generated on an NVIDIA RTX A4500, PyTorch 2.10, CUDA 12.8), numpy, matplotlib, and scikit-learn (real-data study only).
All experiments use fixed seed 42 (auxiliary generators seeded as noted in each script).

Layout

rev2/lib.py — shared library: single-superimposed-signal channel (Rayleigh / Rician / Nakagami fading, complex phase residuals, timing offsets, CSI error), UWCA decoder (active-set masking, top-k masking, I/Q input), closed-form LMMSE receivers, training loops (multi-task meta-training and first-order MAML), evaluation metrics.
rev2/e1_fair_baselines.py … rev2/e9_topk_online.py — one script per experiment (see the table below). *_v2/_v3 scripts supersede their earlier versions where present.
rev2/data/ — raw JSON results behind every quoted number.
rev2/plot_rev2.py — regenerates every new figure from rev2/data/ without rerunning experiments.
legacy/ — scripts for the pre-revision figures (synthetic three-scenario study, attention maps, beta sweep, real-data study).
fig/ — figure PDFs as included in the manuscript.

Detailed per-experiment documentation (setup, procedure, metrics, key results, reviewer-concern map): docs/EXPERIMENTS.md.

Figure/number → script → data map

Manuscript item	Script	Data
Fig. SER vs SNR (3 scenarios)	`legacy/semantic_correlation_sim.py`, `legacy/maml_semantic.py`	`legacy/results/`
Fig. fairness vs optimal linear receivers	`rev2/e1_fair_baselines.py`	`rev2/data/e1_fair_baselines.json`
Fig. residual orthogonality	`rev2/e6_residual_orth.py`	`rev2/data/e6_residual_orth.json`
Phase-error robustness (complex model, CSI error)	`rev2/e2_phase_iui.py`	`rev2/data/e2_phase_iui.json`
Timing-offset robustness	`rev2/e4_v3_async.py`	`rev2/data/e4_v3_async.json`
Dynamic user population	`rev2/e3_dynamic_users.py`	`rev2/data/e3_dynamic_users.json`
Nonlinear view-network study	`rev2/e5_nonlinear.py`	`rev2/data/e5_nonlinear.json`
Meta-adaptation beyond SNR (OOD)	`rev2/e7_v2_meta.py`	`rev2/data/e7_v2_meta.json`
End-to-end anti-collapse study	`rev2/e8_v2_e2e.py`	`rev2/data/e8_v2_e2e.json`
Online top-k acquisition and timing	`rev2/e9_topk_online.py`	`rev2/data/e9_topk_online.json`
Real-data validation	`legacy/revision_realdata_train.py`, `legacy/revision_realdata_plot.py`	`legacy/results/`

Running

python rev2/e1_fair_baselines.py   # writes rev2/data/e1_fair_baselines.json
python rev2/plot_rev2.py           # regenerates the new figure PDFs

Each experiment script is self-contained and writes its JSON into rev2/data/.

Citation and license

To be completed upon acceptance.