This repository contains an implementation of the graph neural network (GNN)-enhanced expectation propagation (EP) for MIMO turbo receivers [1], [2], which extends the GNN-enhanced EP detector (GEPNet) [3] into iterative detection and decoding (IDD).
- Python (>= 3.6)
- Tensorflow (>=2.3.0)
- numpy (>=1.18.5)
- scipy (>=1.4.1)
- Python folder: for training network models, which are saved in the 'model' subdirectory
- Matlab folder (preparing): for performing inference, i.e., iterative turbo receiving
- Training and testing the uncoded GEPNet [3] may be helpful for getting started.
- Run Python codes to train network models, including training APP-based GEPNet, generating extrinsic training LLR datasets (saved in the 'dataset' subdirectory), and training EXT-GEPNet (see [1] and main.py to learn the three-step training in detail).
- Saved the models in the 'model' subdirectory.
- Run Matlab codes to perform EXT-GEPNet-based iterative turbo receiving with the pre-trained weights in the 'model' subdirectory.
- Matlab folder to be added.
- More detailed comments in the source code and steps to carry out the simulations.
[1] X. Zhou, J. Zhang, C.-K. Wen, S. Jin, and S. Han, “Graph neural network-enhanced expectation propagation algorithm for MIMO turbo receivers,” to appear in IEEE Transactions on Signal Processing ([Online] Available: https://arxiv.org/abs/2308.11335).
[2] X. Zhou, J. Zhang, C.-K. Wen, and S. Jin, “Extrinsic graph neural network-aided expectation propagation for Turbo-MIMO receiver,” in Proc. 18th Int. Symp. Wireless Commun. Syst. (ISWCS), Hangzhou, China, Oct. 2022, pp. 1–6.
[3] A. Kosasih, V. Onasis, V. Miloslavskaya, W. Hardjawana, V. Andrean, and B. Vucetic, “Graph neural network aided MU-MIMO detectors,” IEEE J. Sel. Areas Commun., vol. 40, no. 9, pp. 2540–2555, Sep. 2022.
If you have any questions or comments about this work, please feel free to contact xy_zhou@seu.edu.cn