TY - GEN
T1 - Online Beam Learning for Interference Nulling in Hardware-Constrained mm Wave MIMO Systems
AU - Zhang, Yu
AU - Alkhateeb, Ahmed
N1 - Funding Information:
Yu Zhang and Ahmed Alkhateeb are with Arizona State University (Email: y.zhang, alkhateeb@asu.edu). This work is supported by the National Science Foundation under Grant No. 1923676.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. Due to the hardware constraints and the lack of channel knowledge, codebook based beamforming/combining is normally adopted to achieve the desired array gain. Existing codebooks, however, are typically pre-defined and focus only on improving the beamforming gain of their target user, without taking interference into account, which incurs critical performance degradation. In this paper, we propose an efficient deep reinforcement learning approach that learns how to iteratively optimize the beam pattern to null the interference. The proposed solution achieves that while not requiring any explicit channel knowledge of the desired or interfering users and without requiring any coordination with the interferers. Simulation results show that the developed solution is capable of finding a well-shaped beam pattern that significantly suppresses the interference while sacrificing negligible beamforming/combing gain, highlighting a promising solution for dense mmWave/terahertz networks.
AB - Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. Due to the hardware constraints and the lack of channel knowledge, codebook based beamforming/combining is normally adopted to achieve the desired array gain. Existing codebooks, however, are typically pre-defined and focus only on improving the beamforming gain of their target user, without taking interference into account, which incurs critical performance degradation. In this paper, we propose an efficient deep reinforcement learning approach that learns how to iteratively optimize the beam pattern to null the interference. The proposed solution achieves that while not requiring any explicit channel knowledge of the desired or interfering users and without requiring any coordination with the interferers. Simulation results show that the developed solution is capable of finding a well-shaped beam pattern that significantly suppresses the interference while sacrificing negligible beamforming/combing gain, highlighting a promising solution for dense mmWave/terahertz networks.
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U2 - 10.1109/IEEECONF56349.2022.10051931
DO - 10.1109/IEEECONF56349.2022.10051931
M3 - Conference contribution
AN - SCOPUS:85138665327
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 200
EP - 204
BT - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Y2 - 31 October 2022 through 2 November 2022
ER -