IRS-aided Communications Without Channel State Information Relying on Deep Reinforcement Learning
Author:
Affiliation:
1. Graduate School of Information Sciences, Tohoku University,Sendai,Japan
2. Nippon Telegraph and Telephone Corporation,NTT Access Network Service Systems Laboratories,Yokosuka,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001413.pdf?arnumber=10001413
Reference15 articles.
1. Deterministic policy gradient algorithms;silver;31 sh Int Conf Mach Learn (ICML),0
2. Reward-Maximization-Based Passive Beamforming for Multi-RIS-Aided Multi-User MISO Systems
3. Deterministic sampling methods for spheres and SO(3)
4. Learning to Reflect and to Beamform for Intelligent Reflecting Surface With Implicit Channel Estimation
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1. Adaptive Beamforming for Non-Line-of-Sight IRS-Assisted Communications without CSI;2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC);2023-09-05
2. Machine Learning-based Infrastructure Sharing and Shared Operations for Intelligent Reflecting Surface-aided Communications;IEEE Transactions on Cognitive Communications and Networking;2023
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