用多智能体强化学习让基站自动节能，省电超一半。
Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach
- 每个基站独立决策天线配置和休眠模式
- 相比无节能方案省电56.23%，丢包率微增
- 比传统算法更稳，适合动态流量场景
本文研究在动态流量条件下，无蜂窝大规模MIMO（CF mMIMO）网络下行链路的节能问题。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先进休眠模式（ASM）选择。训练完成后，该框架以完全分布式方式运行，无需集中控制，可实时响应流量变化。仿真结果表明，相比无节能方案，功率消耗（PC）降低56.23%；相较于仅使用最轻休眠模式的非学习机制，节省30.12%电力，仅导致轻微丢包率上升。同时，相比广泛使用的深度Q网络（DQN）算法，达到相近功耗水平但显著降低丢包率。
原文摘要 · Abstract (English)
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.
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