DRL-Based Joint AP Deployment and Network-Centric Cluster Formation for Maximizing Long-Term Energy Efficiency in Cell-free Massive MIMO

In cell-free massive MIMO networks, scalability is one of the fundamental problems since a significant number of access points (APs) are widely distributed throughout the network area to cater to the needs of multiple user equipments (UEs). One approach to addressing this issue is through network-centric clustering, which involves dividing the network area into isolated clusters of APs, each connected to its cloud unit (CU). To address these challenges, this paper proposes a deep reinforcement learning (DRL) algorithm that jointly optimizes the network-centric cluster boundaries and decides AP deployment in each cluster to improve long-term energy efficiency. The DRL agent also aims to minimize the average UE drop rate by considering the delay requirements of each UE's requested service. The results show that at least 16% improvement in energy efficiency is obtained compared to the heuristically developed benchmarks.