Cao, H.; Zhu, W.; Feng, W.; Fan, J. Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications. Entropy2022, 24, 326.
Cao, H.; Zhu, W.; Feng, W.; Fan, J. Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications. Entropy 2022, 24, 326.
Cao, H.; Zhu, W.; Feng, W.; Fan, J. Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications. Entropy2022, 24, 326.
Cao, H.; Zhu, W.; Feng, W.; Fan, J. Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications. Entropy 2022, 24, 326.
Abstract
Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite’s precoding matrix and IRS’s phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS’s beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT can achieve approximate performance compared to the upper bound with low complexity.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
