  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.
