Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach

View PDF HTML (experimental) Abstract:This paper focuses on energy savings in downlink operation of cell-free massive MIMO (CF mMIMO) networks under dynamic traffic conditions. We propose a multi-agent deep reinforcement learning (MADRL) algorithm that enables each access point (AP) to autonomously control antenna re-configuration and advanced sleep mode (ASM) selection. After the training process, the proposed framework operates in a fully distributed manner, eliminating the need for centralized control and allowing each AP to dynamically adjust to real-time traffic fluctuations. Simulation results show that the proposed algorithm reduces power consumption (PC) by 56.23% compared to systems without any energy-saving scheme and by 30.12% relative to a non-learning mechanism that only utilizes the lightest sleep mode, with only a slight increase in drop ratio. Moreover, compared to the widely used deep Q-network (DQN) algorithm, it achieves a similar PC level but with a significantly lower drop ratio. Subjects: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2604.07133 [cs.IT] (or arXiv:2604.07133v1 [cs.IT] for this version) https://doi.org/10.48550/arXiv.2604.07133 arXiv-issued DOI via DataCite (pending registration) Submission history From: Qichen Wang [view email] [v1] Wed, 8 Apr 2026 14:27:00 UTC (2,556 KB)