Machine Learning-Based Beamforming Design for Millimeter Wave IRS Communications With Discrete Phase Shifters
Author:
Affiliation:
1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
2. 5G Innovation Centre, Institute of Communication Systems, University of Surrey, Guildford, U.K.
Funder
National Key Research and Development Project
National Natural Science Foundation of China
Young Elite Scientists Sponsorship Program of Henan Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/5962382/9976385/09733391.pdf?arnumber=9733391
Reference15 articles.
1. Secure Transmission for Intelligent Reflecting Surface-Assisted mmWave and Terahertz Systems
2. Enabling Indoor Mobile Millimeter-wave Networks Based on Smart Reflect-arrays
3. CROSS-ENTROPY FOR MONTE-CARLO TREE SEARCH
4. A Tutorial on the Cross-Entropy Method
5. Machine learning inspired energy-efficient hybrid precoding for mmWave massive MIMO systems
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