Scalable Cell-Free Massive MIMO Networks Using Resource-Optimized Backhaul and PSO-Driven Fronthaul Clustering

Abstract

Scalability presents a formidable challenge in traditional Cell-Free (CF) massive Multiple Input Multiple Output (mMIMO) networks, driven by escalating computational demands on access points (APs) and the reliance on a single central processing unit (CPU). To address this, the study proposes a dynamic cooperative clustering (DCC) method, tailored for both backhaul (CPUs-APs) and fronthaul (APs-Users). In the backhaul phase, DCC strategically pairs APs with CPUs using the Kuhn-Munkres algorithm, ensuring equitable resource allocation by considering distance matrices, channel statistics, APs traffic load, and available CPU resources, thereby fairly balancing the distribution of computational load across the CPUs. Subsequently, in the fronthaul phase, the focus is on optimizing the selection of APs for user-centric clusters, using Particle Swarm Optimization (PSO). This optimization aims to maximize the overall sum rate while intelligently managing the inclusion and exclusion of APs within each user-serving cluster. Through extensive simulations, the study highlights the potential of the proposed approach to address scalability concerns in CF-massive MIMO systems, promising improved performance in wireless communication networks. The comparative analysis demonstrates the superiority of the proposed scheme over conventional clustering schemes, consistently delivering better sum rates across various scenarios, with an 18.23% improvement in sum rate and a 30% enhancement in Load Balancing Index (LBI), indicating significantly improved resource distribution and network efficiency.