Computationally Efficient Energy Optimization for Cloud Radio Access Networks With CSI Uncertainty

摘要: This paper studies robust energy optimization for the cloud radio access network (C-RAN). The objective of this is to jointly minimize power consumption through optimizing base station (BS) mode, multi-user (MU)-BS association, and beamforming vectors given imperfect channel state information (CSI). To solve non-trivial problem, we first transform problem a semi-definite programming (SDP) one using S-lemma with aid relaxation technique, then propose SDP-based group sparse approach it iteratively. Since computational complexity solving SDP problems intractable, translate uncertainty in CSI its covariance matrix, recast original as mixed-integer second-order cone problem. We further two-stage rank selection framework determine BS mode MU-BS association separately successively. Simulation results demonstrate convergence our proposed algorithms, validate effectiveness algorithms minimizing C-RAN.