TY - GEN
T1 - Distributed Coordinated Beamforming Based on Multi-Agent Reinforcement Learning in Multicell MISO Systems
AU - Bai, Shaozhuang
AU - Gao, Zhenzhen
AU - Liao, Xuewen
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Maximizing the sum-rate in multicell multiple input single output (MISO) systems is a non convex and NP-hard problem. Most existing algorithms trying to solve this problem are suboptimal with high computational cost and high system interaction overhead. In this paper, we propose a coordinated beamforming (CB) scheme based on multi-agent reinforcement learning (MARL) to maximize the sum-rate of the multicell MISO systems with limited information feedback and exchange. Specifically, the training of the proposed MARL network is guided by the actual sum-rate of the multiple cells, and the execution is performed totally locally by using the local channel quality information feedback. Simulation results show that compared to the existing distributed coordinated beamforming scheme, the proposed scheme achieves similar performance by using much reduced information overhead.
AB - Maximizing the sum-rate in multicell multiple input single output (MISO) systems is a non convex and NP-hard problem. Most existing algorithms trying to solve this problem are suboptimal with high computational cost and high system interaction overhead. In this paper, we propose a coordinated beamforming (CB) scheme based on multi-agent reinforcement learning (MARL) to maximize the sum-rate of the multicell MISO systems with limited information feedback and exchange. Specifically, the training of the proposed MARL network is guided by the actual sum-rate of the multiple cells, and the execution is performed totally locally by using the local channel quality information feedback. Simulation results show that compared to the existing distributed coordinated beamforming scheme, the proposed scheme achieves similar performance by using much reduced information overhead.
KW - Coordinated beamforming
KW - multi-agent rein-forcement learning
KW - multi-input single-output
KW - multicell systems
UR - https://www.scopus.com/pages/publications/85139462271
U2 - 10.1109/ICCC55456.2022.9880773
DO - 10.1109/ICCC55456.2022.9880773
M3 - 会议稿件
AN - SCOPUS:85139462271
T3 - 2022 IEEE/CIC International Conference on Communications in China, ICCC 2022
SP - 446
EP - 450
BT - 2022 IEEE/CIC International Conference on Communications in China, ICCC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/CIC International Conference on Communications in China, ICCC 2022
Y2 - 11 August 2022 through 13 August 2022
ER -