Joint Design of Digital and Analog Processing for Downlink C-RAN with Large-Scale Antenna Arrays

TL;DR

This paper proposes a joint optimization framework for hybrid beamforming in large-scale antenna array C-RAN systems, balancing digital, RF, and fronthaul constraints to maximize downlink sum-rate.

Contribution

It introduces an iterative algorithm for jointly optimizing digital beamforming, fronthaul quantization, and RF beamforming under practical constraints.

Findings

The proposed method improves downlink sum-rate performance.

Numerical simulations validate the effectiveness of the joint optimization approach.

The algorithm effectively handles fronthaul and power constraints in hybrid beamforming.

Abstract

In millimeter-wave communication systems with large-scale antenna arrays, conventional digital beamforming may not be cost-effective. A promising solution is the implementation of hybrid beamforming techniques, which consist of low-dimensional digital beamforming followed by analog radio frequency (RF) beamforming. This work studies the optimization of hybrid beamforming in the context of a cloud radio access network (C-RAN) architecture. In a C-RAN system, digital baseband signal processing functionalities are migrated from remote radio heads (RRHs) to a baseband processing unit (BBU) in the "cloud" by means of finite-capacity fronthaul links. Specifically, this work tackles the problem of jointly optimizing digital beamforming and fronthaul quantization strategies at the BBU, as well as RF beamforming at the RRHs, with the goal of maximizing the weighted downlink sum-rate. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraints on the RF beamforming matrices. An iterative algorithm is proposed that is based on successive convex approximation and on the relaxation of the constant modulus constraint. The effectiveness of the proposed scheme is validated by numerical simulation results.