Energy Saving for Cell-Free Massive MIMO Networks : A Multi-Agent Deep Reinforcement Learning Approach

Sammanfattning

This thesis focuses on energy savings in the downlink operation of Cell- Free Massive MIMO (CF-mMIMO) networks under real-time dynamic traffic conditions. We propose a Multi-Agent Reinforcement Learning (MARL) algorithm that allows each Access Point (AP) to autonomously manage its antenna switching and Advanced Sleep Mode (ASM) transitions. After the training process, the framework functions in a fully distributed manner, removing the need for centralized control and enabling each AP to adjust dynamically to real-time traffic fluctuations. Simulation results demonstrate that the proposed approach achieves up to 64.4% energy savings compared to systems without an energy-saving scheme, and 53.2% savings over systems that use only the active or the lightest sleep mode.