Double deep learning for joint phase-shift and beamforming based on cascaded channels in RIS-assisted MIMO networks
This paper 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 sourceto-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 paper, 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.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
IEEE Wireless Communications LettersVolume
12Issue
4Pages
659 - 663Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2023-01-10Publication date
2023-01-19Copyright date
2023ISSN
2162-2337eISSN
2162-2345Publisher version
Language
- en