Beamforming and Artificial Noise Design for Energy Efficient Cloud RAN with CSI Uncertainty

Abstract

To facilitate green and secure communications, the energy efficiency (EE) and physical (PHY) layer security are desirable for cloud radio access network (C-RAN). However, the problem of EE optimization with guaranteed PHY layer security in C-RAN is challenging, especially in the presence of channel state information (CSI) uncertainty of both information receivers (IRs) and eavesdropper receivers (ERs). Thereby, in this paper, we investigate the worst-case EE maximization problem in a downlink C-RAN, subject to limited power budget of each BS and infinite number of PHY layer security constraints, which is a non-convex fractional programming problem and is NP-hard even in the event of perfect CSI. To solve this non-trivial problem, a fast-converging algorithm is developed. Specifically, based on successive convex approximation (SCA) technique, we transform the original problem into a semi-definite program (SDP) one, allowing for solving it iteratively. The tightness of the SDR is proved, and the SCA algorithm is also proved to converge to a Karush- Kuhn-Tucker point. Extensive simulations are carried out to verify the effectiveness of the proposed algorithm.