Code and stored results for AI-native multi-user semantic communications (JSAC submission)

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