Double Deep Learning for Joint Phase-Shift and Beamforming Based on Cascaded Channels in RIS-Assisted MIMO Networks
Kaiyue Li, Chong Huang, Yu Gong, Gaojie Chen
Abstract
This letter investigates machine learning approach for the joint optimal phase shift and beamforming in the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) network, consisting of one source node, one RIS panel and one destination node. If individual source-to-RIS and RIS-to-destination channels are known, the joint optimization is similar to that in the traditional MIMO network, which has been well studied. However, the channel estimation for the individual channels is complicated and often inaccurate. On the other hand, while estimating the cascaded channels for the source-RIS-destination links are more accessible, the corresponding joint optimization is complicated. In this letter, we propose a novel double deep learning network model which is superior to the conventional reinforcement learning in the RIS joint optimization. Numerical simulations are given to verify the proposed algorithm.