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.

翻译：本文聚焦于动态流量条件下无蜂窝大规模MIMO（CF mMIMO）网络下行链路运行的节能问题。我们提出一种多智能体深度强化学习（MADRL）算法，使每个接入点（AP）能够自主控制天线重构与高级睡眠模式（ASM）选择。经过训练后，所提框架以完全分布式方式运行，无需集中控制，每个AP可根据实时流量波动动态调整。仿真结果表明，与未采用任何节能方案的系统相比，所提算法降低功耗（PC）56.23%；相较于仅使用最浅睡眠模式的非学习机制，降低功耗30.12%，而丢包率仅略有增加。此外，与广泛使用的深度Q网络（DQN）算法相比，该算法在实现相近功耗水平的同时，丢包率显著更低。