Reproducibility package: UWCA semantic multiple access (TWC submission)

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+# UWCA Semantic Multiple Access — Reproducibility Package
+
+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.
+
+## 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
+
+```bash
+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.