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Hybrid beamforming is known to be a cost-effective and wide-spread solutionfor a system with large-scale antenna arrays. This work studies theoptimization of the analog and digital components of the hybrid beamformingsolution for remote radio heads (RRHs) in a downlink cloud radio access network(C-RAN) architecture. Digital processing is carried out at a basebandprocessing unit (BBU) in the "cloud" and the precoded baseband signals arequantized prior to transmission to the RRHs via finite-capacity fronthaullinks. In this system, we consider two different channel state information(CSI) scenarios: 1) ideal CSI at the BBU 2) imperfect effective CSI.Optimization of digital beamforming and fronthaul quantization strategies atthe BBU as well as analog radio frequency (RF) beamforming at the RRHs is acoupled problem, since the effect of the quantization noise at the receiverdepends on the precoding matrices. The resulting joint optimization problem isexamined with the goal of maximizing the weighted downlink sum-rate and thenetwork energy efficiency. Fronthaul capacity and per-RRH power constraints areenforced along with constant modulus constraint on the RF beamforming matrices.For the case of perfect CSI, a block coordinate descent scheme is proposedbased on the weighted minimum-mean-square-error approach by relaxing theconstant modulus constraint of the analog beamformer. Also, we present theimpact of imperfect CSI on the weighted sum-rate and network energy efficiencyperformance, and the algorithm is extended by applying the sample averageapproximation. Numerical results confirm the effectiveness of the proposedscheme and show that the proposed algorithm is robust to estimation errors.

Joint Design of Fronthauling and Hybrid Beamforming for Downlink C-RAN Systems