Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems-Reference-Cited by-同舟云学术

Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems

Published:2022-07 Issue:7 Volume:11 Page:1493-1497
ISSN:2162-2337
Container-title:IEEE Wireless Communications Letters
language:
Short-container-title:IEEE Wireless Commun. Lett.

Author:

Xu Wangyang¹^ORCID,An Jiancheng¹^ORCID,Huang Chongwen²^ORCID,Gan Lu¹^ORCID,Yuen Chau³^ORCID

Affiliation:

1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. College of Information Science and Electronic Engineering and the Zhejiang–Singapore Innovation and AI Joint Research Lab and Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang University, Hangzhou, China

3. Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore

Funder

China National Key Research and Development Program

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Zhejiang University Education Foundation Qizhen Scholar Foundation

Fundamental Research Funds for the Central Universities

Yibin Science and Technology Planning Program

Singapore Ministry of Education Tier 2

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/5962382/9823160/09779399.pdf?arnumber=9779399

Reference10 articles.

1. A robust deep learning-based beamforming design for RIS-assisted multiuser MISO communications with practical constraints;xu;IEEE Trans Cogn Commun Netw,2021

2. Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks

3. Low-Complexity Channel Estimation and Passive Beamforming for RIS-Assisted MIMO Systems Relying on Discrete Phase Shifts

4. Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications

5. Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

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