Joint Beamforming and Remote Radio Head Selection in Limited Fronthaul C-RAN

Abstract

This paper considers the power minimization problem in downlink of cloud radio access networks with limited fronthaul capacity. A joint design of beamforming, remote radio head (RRH) selection and RRH-user association that explicitly takes into account per- fronthaul capacity constraints is considered. The problem of interest is in fact a combinatorial program which is generally NP- hard. We naturally write the considered problem as a mixed integer program by introducing binary selection variables. The challenge is that even if these binary selection variables are relaxed to be continuous, the resulting problem is still nonconvex. For such a problem, finding a high- quality solution, rather than an optimal one, is a more realistic goal. Towards this end we propose two iterative algorithms to deal with combinatorial nature of the joint design problem. In the first method, by novel transformations, we iteratively approximate the continuous nonconvex constraints by convex conic ones using successive convex approximation framework. More explicitly the problem arrived at each iteration of the first method is a mixed-integer second order cone program (MISOCP) for which dedicated solvers are available. The second method is a simplified variant of the first one where we further relax the binary variables in each iteration to be continuous. That is to say, the second method merely requires solving a sequence of SOCPs. After convergence, we then perform a postprocessing procedure on the relaxed selection variables to search for a high-performance solution. Numerical results are presented to demonstrate the superiority of the proposed algorithms over existing methods based on sparse-inducing norm.