Energy efficiencymaximization for full duplexMIMO cloud radio access networks

Use your smartphone to scan this QR code and download this article ABSTRACT This paper studies the energy efficiency (EE) in full-duplex (FD) multiple-input multiple-output (MIMO) cloud radio access networks (CRANs). A cloud control unit (CU) transfers information signals with multiple downlink (DL) and uplink (UL) users through FD radio units (RUs) by limited capacity FH links. In DL transmission, the signals intended to the DL users are centrally processed at the CU and, then, compressed to transfer to the RUs before they are forwarded to the DL users. On the other hand, the signals from the UL users to the RUs are compressed and forwarded to the CU for signal detection processing. Thus, the precoding designs and compression strategies for the UL and DL transmission are critical for the system performance. The conventional methods commonly focus on maximizing the spectral efficiency (SE) in the networks. Although the SE maximization based methods can offer the superior SE performance, they can result in the inefficient usage of the energy in the networks. Thus, this paper focuses on the joint design of the precoders and quantization covariance matrices to maximize the EE. The EE maximization problem is formulated as an optimization problem of the precoders and quantization covariancematrices subject to the transmit power constraints at each RU, each user and the limited capacity of FH links. To deal with the non-convexity of the formulated design problem, we exploit a successive convex approximation (SCA) method to derive the concave lower bound function of the sum rate and a convex upper bound of capacity functions of FH links. Then, the Dinkelbach approach is exploited to derive an efficient iterative algorithm (IA) guaranteeing convergence. Numerical results are given to demonstrate the EE performance of the proposed algorithm.