… remains a challenge for mmWave systems. In this article, we present a … reinforcement
learning (DRL) approach to address this challenge. By learning the blockage pattern, the system …

… In particular, we propose a distributional reinforcement learning (DRL) approach to model
the distribution function of the downlink rate, and, then, the IR reflection coefficient is optimized …

… at the baseband, especially for systems with arbitrary or unknown … is to design a reinforcement
learning based approach to … millimeter wave systems,” IEEE Trans. Commun., vol. 64, no. …

… Based on this issue, in this paper, we propose to use reinforcement learning to manage
the non line of sight (NLOS) scenario. Specifically, we build a model simulating blocked LOS …

… millimeter wave massive multiple-input multiple-output (MIMO) system based on deep
reinforcement learning (… , we propose a novel DRLbased method called PrecoderNet to design the …

… We integrate a reinforcement learning (RL) algorithm, called adaptive Thompson sampling
(ATS), into MAMBA and use it to select a good beam/MCS pair for the next downlink …

… Love, “On the energy efficiency of MIMO hybrid beamforming for millimeter-wave systems
with nonlinear power amplifiers,” IEEE Trans. Wireless Commun., vol. 17, no. …

… coordinated beamforming for highly-mobile millimeter wave systems,” IEEE Access, vol. 6, …
Sabharwal, CE Koksal, and NB Shroff, “Efficient beam alignment in millimeter wave systems …

… In this paper, we presented a reinforcement learning approach for the hybrid beamforming
problem. We give a computational complexity analysis for our proposed algorithm. Finally, we …

… training overhead and the achievable data rate must be considered. In this paper, we propose
an adaptive beam training algorithm using deep reinforcement learning … training methods …