This repository contains an implementation of the conjugate gradient (CG)-based OAMP-NET [1, 2], which utilizes the CG method to avoid the matrix inverse in OAMP iterations and improves the original OAMP through deep learning [3].
- Python (>= 3.6)
- Tensorflow (>=2.3.0)
- numpy (>=1.18.5)
- scipy (>=1.4.1)
WINNER II datasets in Google drive
Massive MIMO 16x16 Over The Air Channel Information in download url
Step 2. Download the WINNER-II model from the provided URL and put the data in the 'winner_model' folder.
For more details, please refer to the readme.txt and the source files in this repository.
We would like to thank Prof. Lei Liu from Japan Advanced Institute of Science and Technology for generally sharing the source code of Memory AMP [4] (a baseline algorithm in our comparison [1]) and discussing the details of the algorithm. The National Sun Yat-sen University Antenna Laboratory led by Prof. K. L. Wong is also highly appreciated for designing the antennas at the prototying platform used in our work [1].
[1] X. Zhou, J. Zhang, C.-W. Syu, C.-K. Wen, J. Zhang, and S. Jin, “Model-driven deep learning-based MIMO-OFDM detector: Design, Simulation, and Experimental Results,” IEEE Trans. Commun., vol. 70, no. 8, pp. 5193-5207, Aug. 2022.
[2] X. Zhou, J. Zhang, C.-K. Wen, J. Zhang, and S. Jin, “Model-driven deep learning-based signal detector for CP-free MIMO-OFDM systems,” in Proc. IEEE Int. Conf. on Commun. (ICC) Workshop, Jun. 2021, pp. 1–6.
[3] H. He, C. Wen, S. Jin, and G. Y. Li, “Model-driven deep learning for MIMO detection,” IEEE Trans. Signal Process., vol. 68, pp. 1702–1715, Feb. 2020.
[4] L. Liu, S. Huang, and B. M. Kurkoski, “Memory AMP,” Dec. 2020, [Online] Available: https://arxiv.org/abs/2012.10861.
If you have any questions or comments about this work, please feel free to contact xy_zhou@seu.edu.cn