Scalable User-Centric Distributed Massive MIMO Systems With Restricted Processing Capacity

This paper investigates the performance of scalable user-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO) systems with multiple central processing units (CPUs), commonly called cell-free mMIMO. Specifically, a framework incorporating processing capacity and inter-CPU communication constraints is proposed. Two methods are presented for limiting the number of radio units (RUs) serving each user equipment (UE). The first method is performed by the CPUs, while the second one is implemented at the UEs and RUs. Both methods prevent the computational complexity (CC) for channel estimation and precoding signals from increasing with the number of RUs. The backhaul signaling demands are presented and modeled, and it is considered that each CPU can serve only a restricted number of UEs managed by other CPUs to mitigate inter-CPU communication. Two strategies to adjust the RU clusters according to the network implementations are also proposed. We compare the proposed approaches with a traditional scalable UC system. Simulation results reveal that the proposed techniques allow UC systems to keep their spectral efficiency (SE) under minor degradation while reducing the CC by 98% and improving energy efficiency (EE). Besides, managing inter-CPU communication controls backhaul traffic effectively, and RU cluster adjustments further reduce CC.