Layered Group Sparse Beamforming for Cache-Enabled Green Wireless Networks

TL;DR

This paper proposes a layered group sparse beamforming framework for cache-enabled wireless networks to minimize power consumption by optimizing base station selection, content assignment, and multicast beamforming, considering QoS and backhaul constraints.

Contribution

It introduces a novel LGSBF modeling framework and a convex approximation algorithm to efficiently reduce power consumption in cache-enabled wireless networks.

Findings

Caching significantly reduces power consumption in dense networks.

The proposed algorithm effectively induces sparsity in beamformers.

Caching benefits are greater with higher user density and less efficient backhaul.

Abstract

The exponential growth of mobile data traffic is driving the deployment of dense wireless networks, which will not only impose heavy backhaul burdens, but also generate considerable power consumption. Introducing caches to the wireless network edge is a potential and cost-effective solution to address these challenges. In this paper, we will investigate the problem of minimizing the network power consumption of cache-enabled wireless networks, consisting of the base station (BS) and backhaul power consumption. The objective is to develop efficient algorithms that unify adaptive BS selection, backhaul content assignment and multicast beamforming, while taking account of user QoS requirements and backhaul capacity limitations. To address the NP-hardness of the network power minimization problem, we first propose a generalized layered group sparse beamforming (LGSBF) modeling framework, which helps to reveal the layered sparsity structure in the beamformers. By adopting the reweighted $\left.\ell_{1}\right/\ell_{2}$-norm technique, we further develop a convex approximation procedure for the LGSBF problem, followed by a three-stage iterative LGSBF framework to induce the desired sparsity structure in the beamformers. Simulation results validate the effectiveness of the proposed algorithm in reducing the network power consumption, and demonstrate that caching plays a more significant role in networks with higher user densities and less power-efficient backhaul links.