Group sparse beamforming for multicast green Cloud-RAN via parallel semidefinite programming

Abstract

The Cloud radio access network (Cloud-RAN) has great potentials to improve energy efficiency and increase capacity of wireless networks. In this paper, we investigate multicast beamforming design for network power minimization of Cloud-RAN, which is shown to be a highly intractable non-convex mixed integer non-linear programming problem. To provide an efficient solution to this highly complicated problem, we propose a three-stage algorithm based on the group-sparsity inducing norm, which minimizes network power by coordinated multicast beamforming and adaptively selecting active remote radio heads (RRHs). In particular, a novel quadratic variational weighted ℓ₁=ℓ₂-norm aided alternating algorithm is proposed to exploit the group-sparsity structure of the beamforming vector, thereby guiding the active RRH set selection. Given the selected RRH set, multicast beamforming is performed to minimize the network power consumption. Furthermore, to enhance the computation efficiency upon utilizing the shared computing resources in the cloud center, we employ the alternating direction method of multipliers (ADMM) algorithm to solve the resulting semidefinite programming problems in parallel. Extensive simulation results will demonstrate the effectiveness of the proposed multicast group sparse beamforming algorithm.