Supervised AutoEncoder-Based Beamforming Approach for Satellite mmWave Communication

Abstract

Beamforming is a technique commonly used in wireless communication systems to enhance the signal quality of a receiver. In this study, we compare the performance of an encoder-based beamformer with convolutional neural network (CNN) and minimum variance distortionless response (MVDR) approaches in terms of signal-to-interference-plus-noise ratio (SINR). Our results show that the encoder-based approach achieved an average SINR of 25.82 dB, while the CNN approach achieved an average SINR of 22.40 dB and the MVDR approach achieved an average SINR of 17.64 dB. The performance of the encoder-based approach was found to be superior to that of the CNN approach but much superior to that of the MVDR approach. The encoder-based approach outperformed the CNN approach by 3.42 dB and MVDR approach by 8.18 dB on average. In addition, the unique contribution of our encoder-based approach is presenting a new perspective on beamforming in mmWave communication. We further discuss its potential impact on addressing challenges related to LEO satellite systems.