Dynamic Backhaul Clustering for Enhanced Scalability in Cell-Free Massive MIMO Networks

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks are emerging as a promising technology for next-generation wireless communication. However, as the number of users increases in a CF-mMIMO network, scalability and optimal network performance become challenging. To tackle this issue, we propose a novel approach of dynamically clustering access points (APs) based on central processing unit (CPU) resources. The proposed method optimizes AP clustering by considering CPU resources, including bandwidth and power, distance, channel conditions, and APs' data demands. The joint optimization framework aims to resolve scalability issues and maximize network performance by balancing channel conditions, CPUs' computational strengths, and the user's varying data demands. The results from the simulations confirm that the proposed method effectively enhances both the network's scalability and performance.