TY - GEN
T1 - Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
AU - Zhang, Yu
AU - Alrabeiah, Muhammad
AU - Alkhateeb, Ahmed
N1 - Funding Information:
Yu Zhang, Muhammad Alrabeiah and Ahmed Alkhateeb are with Arizona State University (Email: y.zhang,malrabei, alkhateeb@asu.edu). This work is supported by the National Science Foundation under Grant No. 1923676.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Deploying large scale antenna arrays is a key characteristic of current and future wireless communication systems. However, due to some non-ideal practical conditions, such as the unknown array geometry or possible hardware impairments, the accurate channel state information becomes hard to acquire. This impedes the design of beamforming/combining vectors that are crucial to fully exploit the potential of the large-scale MIMO systems or to combat the high path-loss in millimeter wave (mmWave) communications. In this paper, we propose a novel solution that leverages deep reinforcement learning (DRL) to learn the beam pattern that is optimized for a group of users without the explicit knowledge of the channels. Simulation results show that the developed solution is capable of finding the near optimal beam pattern with quantized phase shifters and with only requiring the beamforming gain feedback from the users.
AB - Deploying large scale antenna arrays is a key characteristic of current and future wireless communication systems. However, due to some non-ideal practical conditions, such as the unknown array geometry or possible hardware impairments, the accurate channel state information becomes hard to acquire. This impedes the design of beamforming/combining vectors that are crucial to fully exploit the potential of the large-scale MIMO systems or to combat the high path-loss in millimeter wave (mmWave) communications. In this paper, we propose a novel solution that leverages deep reinforcement learning (DRL) to learn the beam pattern that is optimized for a group of users without the explicit knowledge of the channels. Simulation results show that the developed solution is capable of finding the near optimal beam pattern with quantized phase shifters and with only requiring the beamforming gain feedback from the users.
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U2 - 10.1109/IEEECONF51394.2020.9443430
DO - 10.1109/IEEECONF51394.2020.9443430
M3 - Conference contribution
AN - SCOPUS:85102376769
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 445
EP - 449
BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Y2 - 1 November 2020 through 5 November 2020
ER -