61 lines
2.9 KiB
Markdown
Executable File
61 lines
2.9 KiB
Markdown
Executable File
# UWCA Semantic Multiple Access — Reproducibility Package
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Simulation code, raw results, and figure sources for the manuscript
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"Relevance-Aware Semantic Multiple Access via Meta-Learned User-Wise
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Cross-Attention" (submitted to IEEE Transactions on Wireless Communications).
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## Requirements
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- Python 3.10+ with `torch` (CUDA optional; results generated on an NVIDIA
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RTX A4500, PyTorch 2.10, CUDA 12.8), `numpy`, `matplotlib`, and
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`scikit-learn` (real-data study only).
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- All experiments use fixed seed 42 (auxiliary generators seeded as noted in
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each script).
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## Layout
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- `rev2/lib.py` — shared library: single-superimposed-signal channel
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(Rayleigh / Rician / Nakagami fading, complex phase residuals, timing
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offsets, CSI error), UWCA decoder (active-set masking, top-k masking,
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I/Q input), closed-form LMMSE receivers, training loops (multi-task
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meta-training and first-order MAML), evaluation metrics.
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- `rev2/e1_fair_baselines.py` … `rev2/e9_topk_online.py` — one script per
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experiment (see the table below). `*_v2/_v3` scripts supersede their
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earlier versions where present.
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- `rev2/data/` — raw JSON results behind every quoted number.
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- `rev2/plot_rev2.py` — regenerates every new figure from `rev2/data/`
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without rerunning experiments.
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- `legacy/` — scripts for the pre-revision figures (synthetic three-scenario
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study, attention maps, beta sweep, real-data study).
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- `fig/` — figure PDFs as included in the manuscript.
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## Figure/number → script → data map
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| Manuscript item | Script | Data |
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| Fig. SER vs SNR (3 scenarios) | `legacy/semantic_correlation_sim.py`, `legacy/maml_semantic.py` | `legacy/results/` |
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| Fig. fairness vs optimal linear receivers | `rev2/e1_fair_baselines.py` | `rev2/data/e1_fair_baselines.json` |
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| Fig. residual orthogonality | `rev2/e6_residual_orth.py` | `rev2/data/e6_residual_orth.json` |
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| Phase-error robustness (complex model, CSI error) | `rev2/e2_phase_iui.py` | `rev2/data/e2_phase_iui.json` |
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| Timing-offset robustness | `rev2/e4_v3_async.py` | `rev2/data/e4_v3_async.json` |
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| Dynamic user population | `rev2/e3_dynamic_users.py` | `rev2/data/e3_dynamic_users.json` |
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| Nonlinear view-network study | `rev2/e5_nonlinear.py` | `rev2/data/e5_nonlinear.json` |
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| Meta-adaptation beyond SNR (OOD) | `rev2/e7_v2_meta.py` | `rev2/data/e7_v2_meta.json` |
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| End-to-end anti-collapse study | `rev2/e8_v2_e2e.py` | `rev2/data/e8_v2_e2e.json` |
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| Online top-k acquisition and timing | `rev2/e9_topk_online.py` | `rev2/data/e9_topk_online.json` |
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| Real-data validation | `legacy/revision_realdata_train.py`, `legacy/revision_realdata_plot.py` | `legacy/results/` |
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## Running
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```bash
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python rev2/e1_fair_baselines.py # writes rev2/data/e1_fair_baselines.json
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python rev2/plot_rev2.py # regenerates the new figure PDFs
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```
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Each experiment script is self-contained and writes its JSON into
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`rev2/data/`.
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## Citation and license
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To be completed upon acceptance.
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