System cost minimization in cloud RAN with limited fronthaul capacity

Abstract

Cloud radio access network (C-RAN) is emerging as a potential alternative for the next generation RAN by merging RAN and cloud computing together. In this paper, we consider the baseband unit (BBU) pool of C-RAN as a collection of virtual machines (VMs). We allow each user equipment (UE) to associate with multiple VMs in the BBU pool, and each remote radio head (RRH) can only serve a limited number of UEs. Under this model, we jointly optimize the VM activation in the BBU pool and sparse beamforming in the coordinated RRH cluster, which is constrained by limited fronthaul capacity, to minimize the system cost of C-RAN. We formulate this problem as a mixed-integer nonlinear programming problem, and then propose efficient methods to optimize the number of active VMs, as well as the sparse beamforming vectors. Moreover, we derive a closed-form solution for the beamforming vectors. Simulation results suggest that our proposed algorithms have better performance than the benchmark algorithms in terms of both system cost and robustness.