70 lines
3.6 KiB
Markdown
Executable File
70 lines
3.6 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 the IEEE Transactions on Communications).
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The package contains everything needed to regenerate every number and
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figure in the manuscript: one script per study, the raw JSON/CSV results
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each script produced, and a single plotting script that rebuilds the figure
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PDFs from the stored results without rerunning any experiment.
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**Detailed per-study documentation (question, setup, procedure, metrics,
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key results): [docs/EXPERIMENTS.md](docs/EXPERIMENTS.md).**
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## Requirements
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- Python 3.10+ with `torch` (results generated on an NVIDIA RTX A4500,
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PyTorch 2.10, CUDA 12.8; CPU fallback works), `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
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in each script).
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## Layout
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- `experiments/lib.py` — shared library: the single-superimposed-signal
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uplink of the manuscript (Rayleigh / Rician / Nakagami fading, complex
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phase residuals, timing offsets, CSI error; fixed disjoint transmit block
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masks), the UWCA decoder (learned soft masks, active-set masking, top-k
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masking, optional I/Q input), closed-form LMMSE reference receivers,
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training loops, and evaluation metrics.
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- `experiments/e1_fair_baselines.py` … `experiments/e9_topk_online.py` —
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one self-contained script per study (see the table below).
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- `experiments/data/` — raw JSON results behind every quoted number.
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- `experiments/make_figures.py` — regenerates the figure PDFs from
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`experiments/data/` only.
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- `simulation/` — the three-scenario synthetic study, hyperparameter
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ablations, threshold sweeps, and the real-data study.
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- `fig/` — figure PDFs as included in the manuscript.
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## Study → script → data map
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| Manuscript item | Script | Data |
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| SER vs SNR, three scenarios (Fig. 2) | `simulation/semantic_correlation_sim.py`, `simulation/maml_semantic.py` | `simulation/results/` |
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| Optimal-linear-receiver fairness (Fig. 3) | `experiments/e1_fair_baselines.py` | `experiments/data/e1_fair_baselines.json` |
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| Residual orthogonality (Fig. 4) | `experiments/e6_residual_orth.py` | `experiments/data/e6_residual_orth.json` |
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| Complex phase-error model, CSI error | `experiments/e2_phase_iui.py` | `experiments/data/e2_phase_iui.json` |
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| Timing offsets and realignment | `experiments/e4_async.py` | `experiments/data/e4_async.json` |
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| Dynamic user population | `experiments/e3_dynamic_users.py` | `experiments/data/e3_dynamic_users.json` |
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| Nonlinear view-network study | `experiments/e5_nonlinear.py` | `experiments/data/e5_nonlinear.json` |
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| Adaptation across fading families | `experiments/e7_meta.py` | `experiments/data/e7_meta.json` |
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| End-to-end training with anti-collapse | `experiments/e8_e2e.py` | `experiments/data/e8_e2e.json` |
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| Online top-k acquisition and timing | `experiments/e9_topk_online.py` | `experiments/data/e9_topk_online.json` |
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| beta sweep, attention maps, ablations | `simulation/betasweep.py etc.`, `simulation/plot_figures.py` | `simulation/results/` |
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| Real-data validation (Fig. 5) | `simulation/realdata_train.py`, `simulation/realdata_plot.py` | `simulation/results/` |
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## Running
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```bash
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python experiments/e1_fair_baselines.py # writes experiments/data/e1_fair_baselines.json
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python experiments/make_figures.py # rebuilds the figure PDFs from data/
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```
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Each study script is self-contained and writes its JSON into
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`experiments/data/`.
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## Citation and license
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To be completed upon acceptance.
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