Group Sparse Beamforming for Green Cloud-RAN

TL;DR

This paper introduces a novel group sparse beamforming approach for green Cloud-RAN, optimizing RRH selection and power to reduce overall network and transport link power consumption effectively.

Contribution

It proposes a new joint RRH selection and power minimization framework using group sparse beamforming with weighted norm minimization, improving energy efficiency.

Findings

Significant reduction in network power consumption.

Effective RRH selection with near-optimal performance.

Highlighting the importance of transport link power considerations.

Abstract

A cloud radio access network (Cloud-RAN) is a network architecture that holds the promise of meeting the explosive growth of mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost infrastructure. However, as all the RRHs need to be connected to the BBU pool through optical transport links, the transport network power consumption becomes significant. In this paper, we propose a new framework to design a green Cloud-RAN, which is formulated as a joint RRH selection and power minimization beamforming problem. To efficiently solve this problem, we first propose a greedy selection algorithm, which is shown to provide near- optimal performance. To further reduce the complexity, a novel group sparse beamforming method is proposed by inducing the group-sparsity of beamformers using the weighted $\ell_1/\ell_2$-norm minimization, where the group sparsity pattern indicates those RRHs that can be switched off. Simulation results will show that the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption.