User-centric Virtualized CPU Deployment and AP Clustering for Scalable Cell-Free Massive MIMO

We consider scalable RAN management for largescale deployment in a distributed central processing units (CPUs) environment with cell-free massive MIMO (CF-mMIMO). In distributed CPUs among multiple sites, there is a problem of low radio quality for users at the site edge areas, which are the boundaries between sites. This is due to inter-site interference between UEs processed by different CPUs and a reduction in received signal power owing to the inability to associate with the surrounding APs connecting to other sites. To address this problem, our approach is to deploy the vCPUs of users at the site edges to higher-level sites based on the physical hierarchical structure of the RAN. This hierarchical deployment allows the formation of broad AP clusters associated with group APs across the sites and improves radio quality. However, deploying vCPUs at higher-level sites causes the radio signal to flow into the backhaul (BH), significantly increasing the transmission load depending on the size of the AP cluster. Thus, we formulate an optimization problem to improve user throughput everywhere under the constraints of the RAN physical resources by managing the deployment of vCPUs and AP clustering. This optimization problem is non-linear and non-convex and requires inverse matrix calculations, resulting in computational complexity. Therefore, we propose a lightweight list-processing algorithm with reference signals of APs around users that does not use inverse matrices calculation and metaheuristic search. Simulation results show that the proposed method improves user throughput and provides lightweight calculation with a large number of UEs compared to existing methods.