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Slim to paper-essential code with explanatory README

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KiHoLee
2026-08-02 17:21:08 +09:00
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@@ -5,46 +5,72 @@ Code and stored results for the manuscript
(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.
This repository contains exactly the code needed to reproduce the
experiments reported in the paper, together with the stored CSV
results from which every figure is regenerated. All experiments use
fixed random seeds.
## What each script does
| Script | Role in the paper |
|---|---|
| `c11_doppler_csi.py` | Shared library. Implements the time-varying tapped-delay-line channel by sample-level time-domain convolution, so intra-symbol Doppler produces genuine inter-carrier interference, plus the pilot-aging frame structure and the aging-aware LMMSE front end of Section IV-A. |
| `c13_mnist.py` | MNIST study of Sections V-A and V-B. Defines the signed user-wise attention receiver, the Transformer shared-embedding separator, and the per-user autoencoder, with joint training, warm-started first-order MAML meta-training, and the SNR-sweep evaluation behind Fig. 6. |
| `c18_mnist_doppler.py` | Doppler sweep behind Fig. 7, with task-conditional decoder-side adaptation at every operating point. |
| `c19_mnist_epoch.py` | Convergence study behind Fig. 5, recording the task-adapted SER at every training checkpoint. |
| `c22_maml_epoch.py` | Budget-matched meta-training trajectory that forms the right-hand segment of Fig. 5. |
| `c21_mnist_flat.py` | Flat Rayleigh study behind Table III, including the decoder-side first-order MAML variant. |
| `c20_bert.py` | Concluding BERT/AG News text study behind Fig. 8, from feature caching to training and evaluation. |
## Reproducing the figures
Each figure regenerates from the stored CSV results without
retraining.
```bash
python3 c19_mnist_epoch.py --mode fig # Fig. 5
python3 c13_mnist.py --mode fig # Fig. 6
python3 c18_mnist_doppler.py --mode fig # Fig. 7
python3 c20_bert.py --mode fig # Fig. 8
```
Table III values are stored in `results_mnist/mnist_flat.csv`, and
Table IV values come from `results_mnist/mnist_results.csv` and
`results_mnist/mnist_doppler.csv`.
## Rerunning the experiments
Full reruns retrain from scratch on the same fixed seeds.
```bash
# MNIST time-varying study (Figs. 5-7, Table IV)
python3 c13_mnist.py --mode train
python3 c13_mnist.py --mode train-tf
python3 c13_mnist.py --mode train-ae
python3 c13_mnist.py --mode train-tx-cls
python3 c13_mnist.py --mode train-maml
python3 c13_mnist.py --mode eval
python3 c18_mnist_doppler.py --mode run
python3 c19_mnist_epoch.py --mode run
python3 c19_mnist_epoch.py --mode run-maml --steps 7500
python3 c22_maml_epoch.py --pretrain-steps 7500
# Flat Rayleigh study (Table III)
python3 c21_mnist_flat.py --mode run
# BERT text study (Fig. 8)
python3 c20_bert.py --mode cache
python3 c20_bert.py --mode train-tx-cls
python3 c20_bert.py --mode train
python3 c20_bert.py --mode train-tf
python3 c20_bert.py --mode train-ae
python3 c20_bert.py --mode train-maml
python3 c20_bert.py --mode eval
```
## 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
WSL2 Ubuntu with Python 3 and CUDA-enabled PyTorch. The BERT study
additionally needs `transformers` and `datasets`. MNIST downloads
automatically through `torchvision`, and AG News through `datasets`
(`fancyzhx/ag_news`).