Performance-cost trade-off of joint beamforming and user clustering in cloud radio access networks

Abstract

Cloud Radio Access Network (CRAN) is a promising network architecture for 5G to address the increasing demand for mobile data traffic. We consider a joint beamforming and clustering (user-to-Remote Radio Head (RRH) association) issue for downlink CRAN to solve the sum-rate maximization problem under fronthaul link capacity and per-RRH power constraints. The main objective is to investigate and analyze the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of imperfect Channel State Information (CSI). We propose a hybrid algorithm which periodically activates dynamic and static clustering strategies to manage the allocation process over time. This algorithm has the benefit to approach the optimal performance while being aware of practical system constraints. Furthermore, we present an analysis of major cost metrics for the proposed and reference dynamic algorithms. The simulation results show that our proposed algorithm reduces significantly the complexity and signaling costs while approaching the performance of the optimal solution.