Fronthaul Compression and Precoding Design for Full-Duplex Cloud Radio Access Network

Abstract

In this paper, joint design of fronthaul compression and precoding is studied for full-duplex (FD) cloud radio access networks. Multiple uplink and downlink users equipped with multiple antennas communicate with a control unit (CU) in the "cloud" through a set of multiantenna FD radio units that are connected to the CU through limited capacity fronthaul links. In the first part of this paper, we address the weighted sum-rate maximization problem, to compute the optimal precoding and the quantization noise covariance matrices. By exploiting the relationship between weighted-sum-rate maximization and weighted minimum-mean-square-error minimization problems, and leveraging the successive convex approximation (SCA) method, we propose an iterative algorithm that guarantees convergence to a stationary point. In the second part of this paper, we address the stochastic sum-rate maximization problem under fast-fading channels, where only the statistics of the channel state information is available. Casting this nonconvex problem as a difference of convex problem, an iterative algorithm based on the combination of stochastic successive upper bound minimization and SCA approaches that guarantees convergence to a stationary point is proposed. Numerical results demonstrate the advantage of the proposed algorithms.