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.

我们开发了一种多智能体强化学习算法，通过对多个大规模 MIMO 基站的多级高级休眠模式和天线切换进行决策，以最小化多个多基站多小区网络的总能耗，同时保持整体服务质量。该问题被建模为分散式部分可观察马尔可夫决策过程 (DEC-POMDP)，以实现个别基站之间的协作，以解决小区间的干扰。设计了一种多智能体近端策略优化 (MAPPO) 算法来学习一个协作基站控制策略。为了增强可扩展性，进一步提出了 MAPPO - 邻近策略的改进版本。仿真结果表明，训练得到的 MAPPO 智能体相比基准策略取得更好的性能。具体而言，与自动休眠模式 1 (符号级睡眠) 算法相比，MAPPO - 邻近策略在低流量小时内减少了约 8.7% 的功耗，在高流量小时内提高了约 19% 的能源效率。