JSAC_AIRAN/README.md

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