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

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 …

advanced agent algorithm bss consumption cs.ai cs.it cs.lg decisions energy making massive math.it multi-agent multiple network quality reinforcement reinforcement learning saving service sleep systems total