Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/11785
Title: | Multi-Agent Reinforcement Learning for Energy Saving in Multi-Cell Massive Mimo Systems | Authors: | Cai, T. Wang, Q. Zhang, S. Demir, O.T. Cavdar, C. |
Keywords: | antenna switching BS control for energy saving massive MIMO multi-agent reinforcement learning Energy efficiency Energy utilization Reinforcement learning Sleep research Antenna switching Base station control for energy saving Energy savings Energy-savings Massive multiple-input multiple-output Multi agent Multi-agent reinforcement learning Multiple inputs Multiple outputs Policy optimization Markov processes |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) to enable collaboration between individual BSs, which is necessary to tackle inter-cell interference. A multi-agent proximal policy optimization (MAPPO) algorithm is designed to learn a collaborative BS control policy. To enhance its scalability, a modified version called MAPPO-neighbor policy is further proposed. Simulation results demonstrate that the trained MAPPO agent achieves better performance compared to baseline policies. Specifically, compared to the auto sleep mode 1 (symbol-level sleeping) algorithm, the MAPPO-neighbor policy reduces power consumption by approximately 8.7% during low-traffic hours and improves energy efficiency by approximately 19% during high-traffic hours, respectively. © 2024 IEEE. | Description: | 1st IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2024 -- 5 May 2024 through 8 May 2024 -- Stockholm -- 201880 | URI: | https://doi.org/10.1109/ICMLCN59089.2024.10624787 https://hdl.handle.net/20.500.11851/11785 |
ISBN: | 979-835034319-9 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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