Scalable AP selection strategies for user-centric cell-free massive MIMO networks

Abstract

User-centric (UC) cell-free (CF) massive multiple-input multiple-output (MIMO) systems are promising technologies for beyond 5G (B5G) networks. In these systems, the user equipment (UE) is associated with a subset of access points (APs) distributed into the coverage area, leading to improvements in macro-diversity and spectral efficiency (SE) compared to conventional cell-based systems. Despite the benefits, challenges such as scalable AP selection strategies, computational complexity (CC), and inter-central processing unit (CPU) coordination may still exist in these systems. In this regard, this thesis proposes a novel and general AP selection framework that affords scalability for UC systems, enabling more efficient use of the network resources, such as transmission power and reduced processing demands. The solution is based on a matched-decision among the most suitable connections for APs and UEs. Moreover, three strategies to fine-tune the AP clusters of UEs are proposed, aiming to reduce the number of APs connected to each UE without compromising the SE. Simulation results reveal that the matched-decision framework improves up to 163% the SE of the 95% likely UEs compared with baseline schemes. A heuristic approach that reduces the effects of inter-CPU coordination is also proposed. It decreases the number of inter-coordinated UEs (i.e., UEs connected to multiple CPUs) on each CPU to reduce signaling demands on backhaul links. Numerical results indicate that the proposed method mitigates inter-CPU coordination while yielding slight degradation in SE and improving energy efficiency (EE). Finally, this thesis investigates the performance of UC systems with limited processing capacity. Specifically, it is assumed that the CC of performing channel estimation and precoding signals does not increase with the number of APs. Thus, the UE can only be associated with a finite number of APs. Furthermore, a method is proposed for adjusting the AP clusters according to the network implementation, i.e., centralized or distributed. The results show that UC systems can keep the SE under minor degradation even if the CC up to 96%. Besides, the proposed method for adjusting the AP cluster leads to further reductions in CC.