51 lines
2.6 KiB
Markdown
Executable File
51 lines
2.6 KiB
Markdown
Executable File
# JSAC_AIRAN
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Code and stored results for the manuscript
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**"AI-Native Multi-User Semantic Communications via Meta-Learned Attention for 6G AI-RAN"**
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(submitted to IEEE Journal on Selected Areas in Communications,
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Special Issue on Towards Open and Intelligent 6G RAN).
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All experiments use fixed random seeds, and every figure in the paper is
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regenerated by a single script from the stored CSV results in this
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repository, without rerunning the experiments.
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## Environment
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- WSL2 Ubuntu, Python 3.14, PyTorch with CUDA (experiments ran on an
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NVIDIA GPU; CPU fallback works for plotting and evaluation)
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- `transformers` and `datasets` (BERT feature extraction only)
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- MNIST downloads automatically via `torchvision`; AG News via
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`datasets` (`fancyzhx/ag_news`)
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## Figure and table map
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| Paper item | Regenerate figure (from stored CSV) | Full rerun |
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| Table III (MNIST, flat Rayleigh) | values in `results_mnist/mnist_flat.csv` | `python3 c21_mnist_flat.py --mode run` |
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| Fig. 5 (convergence + MAML meta phase) | `python3 c19_mnist_epoch.py --mode fig` | `c19 --mode run`, `c19 --mode run-maml --steps 7500`, `c22_maml_epoch.py --pretrain-steps 7500` |
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| Fig. 6 (MNIST SER vs SNR) | `python3 c13_mnist.py --mode fig` | `c13 --mode train / train-tf / train-ae / train-tx-cls / train-maml / eval` |
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| Fig. 7 (MNIST SER vs Doppler) | `python3 c18_mnist_doppler.py --mode fig` | `c18 --mode run` |
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| Fig. 8 (BERT text SER vs SNR) | `python3 c20_bert.py --mode fig` | `c20 --mode cache / train-tx-cls / train / train-tf / train-ae / train-maml / eval` |
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| Table IV (summary) | values in `results_mnist/mnist_results.csv`, `mnist_doppler.csv` | see Fig. 6 and Fig. 7 rows |
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Run every command from the repository root. Trained checkpoints
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(`*.pt`) are included, so `--mode eval` and `--mode fig` work without
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retraining. The BERT feature cache (`results_bert/bert_feats.pt`,
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about 85 MB) is excluded and is regenerated by
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`python3 c20_bert.py --mode cache`.
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## Files
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- `c11_doppler_csi.py` shared library, time-varying TDL channel with
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intra-symbol Doppler (genuine ICI), pilot aging, aging-aware LMMSE
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- `c13_mnist.py` MNIST models (signed, Transformer SE, per-user AE),
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training, MAML meta-training, SNR-sweep evaluation
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- `c18_mnist_doppler.py` MNIST Doppler sweep
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- `c19_mnist_epoch.py` convergence study with task-adapted checkpoints
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- `c20_bert.py` BERT/AG News text study
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- `c21_mnist_flat.py` flat Rayleigh study with decoder-side
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first-order MAML
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- `c22_maml_epoch.py` budget-matched meta-training trajectory
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- `results_mnist/`, `results_bert/` stored CSV results and checkpoints
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- `fig/` figure PDFs as used in the manuscript
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