Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach

TL;DR

This paper introduces a low-complexity, statistically-guided group sparse beamforming method for Cloud-RANs that reduces computational overhead while maintaining performance, using a novel random matrix theory approach.

Contribution

It develops a new smoothed $ ext{l}_p$-minimization algorithm with closed-form solutions and applies random matrix theory to derive deterministic RRH selection criteria based on statistical CSI.

Findings

The proposed $ ext{l}_p$-minimization improves group sparsity induction.

The random matrix approach reduces computational complexity for RRH ordering.

Simulation results confirm performance gains and reduced overhead.

Abstract

Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks (Cloud-RANs), which, however, suffers high computational complexity. In particular, a complex optimization problem needs to be solved to obtain the remote radio head (RRH) ordering criterion in each transmission block, which will help to determine the active RRHs and the associated fronthaul links. In this paper, we propose innovative approaches to reduce the complexity of this key step in group sparse beamforming. Specifically, we first develop a smoothed $\ell_p$-minimization approach with the iterative reweighted-$\ell_2$ algorithm to return a Karush-Kuhn-Tucker (KKT) point solution, as well as enhancing the capability of inducing group sparsity in the beamforming vectors. By leveraging the Lagrangian duality theory, we obtain closed-form solutions at each iteration to reduce the computational complexity. The well-structured solutions provide the opportunities to apply the large-dimensional random matrix theory to derive deterministic approximations for the RRH ordering criterion. Such an approach helps to guide the RRH selection only based on the statistical channel state information (CSI), which does not require frequent update, thereby significantly reducing the computation overhead. Simulation results shall demonstrate the performance gains of the proposed $\ell_p$-minimization approach, as well as the effectiveness of the large system analysis based framework for computing RRH ordering criterion.