Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems

no code implementations • 5 Feb 2024 • Tianzhang Cai, Qichen Wang, Shuai Zhang, Özlem Tuğfe Demir, Cicek Cavdar

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.

Multi-agent Reinforcement Learning Total Energy