Robust Beamforming Optimization for Downlink Cloud Radio Access Networks

Abstract

This paper addresses the channel uncertainty issue in the multi-input multi-output (MIMO) cloud radio access network (C-RAN) by studying the robust beamforming. Our objective is to minimize the overall network power and backhaul cost while guaranteeing the users' SINR constraints for a target proportion of users. The channel state information (CSI) is assumed to be imperfect and the additive channel state information error is modeled as Gaussian distributed variables. We model the total power by ℓ ₀ /ℓ ₂ -norm functions and use the semidefinite programing (SDP) and ℓ ₀ -norm approximation to transform the original problem into tractable ones. Then, probability approach are proposed to deal with the CSI uncertainty and an alternating direction method of multipliers (ADMM) based algorithm is utilized to solve the transformed optimization problem. Simulation results verify that the proposed robust designs can significantly enhance the performance compared the non-robust case and efficiently resolve the channel uncertainty issue.