Robust Group Sparse Beamforming for Multicast Green Cloud-RAN with Imperfect CSI
Yuanming Shi, Jun Zhang, Khaled B. Letaief

TL;DR
This paper proposes a robust three-stage group sparse beamforming algorithm for multicast Cloud-RAN with imperfect CSI, aiming to minimize network power by adaptively selecting active RRHs and designing beamformers.
Contribution
It introduces a novel three-stage algorithm combining group sparsity induction, PhaseLift, and SDR techniques for power-efficient multicast beamforming under imperfect CSI.
Findings
Algorithm converges effectively in simulations.
Significant power savings demonstrated.
Robust beamforming achieved despite CSI imperfections.
Abstract
In this paper, we investigate the network power minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network power minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. However, this yields a non-convex combinatorial optimization problem, for which we propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the weighted mixed l1/l2-norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by…
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