Neural Network Based Optimization of Transmit Beamforming and RIS Coefficients Using Channel Covariances in MISO Downlink
Important
In the previous conference paper, we optimized the transmit beamforming. This is its extended journal version by optimizing both transmit beamforming and RIS coefficients with the use of channel covariances in MISO downlink.
@article{KYAW2025155656,
title = {Neural network based optimization of transmit beamforming and RIS coefficients using channel covariances in MISO downlink},
journal = {AEU - International Journal of Electronics and Communications},
volume = {191},
pages = {155656},
year = {2025},
issn = {1434-8411},
doi = {https://doi.org/10.1016/j.aeue.2024.155656},
url = {https://www.sciencedirect.com/science/article/pii/S1434841124005429},
author = {Khin Thandar Kyaw and Wiroonsak Santipach and Kritsada Mamat and Kamol Kaemarungsi and Kazuhiko Fukawa and Lunchakorn Wuttisittikulkij},
keywords = {Beamforming, Optimization, Downlink, RIS, Channel covariance, MISO, Neural network, Unsupervised learning, Supervised learning},
}
We propose an unsupervised beamforming neural network (BNN) and a supervised reconfigurable intelligent surface (RIS) convolutional neural network (CNN) to optimize transmit beamforming and RIS coefficients of multi-input single-output (MISO) downlink with RIS assistance. To avoid frequent beam updates, the proposed BNN and RIS CNN are based on slow-changing channel covariances and are different from most other neural networks that utilize channel instances. Numerical simulations show that for a small or moderate signal-to-noise ratio (SNR), the proposed BNN with RIS CNN can achieve a sum rate close to that of a system with optimal beams and RIS coefficients. Furthermore, the proposed scheme significantly reduces the computation time.
Please refer the link for implementation details.
