# 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