LAUSR

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This paper studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network architecture. Digital processing is carried out at a baseband processing unit (BBU) in the “cloud,” and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU and 2) imperfect effective CSI. The optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio-frequency (RF) beamforming at the RRHs is a coupled problem since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. The numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors.