Robust Group Sparse Beamforming for Multicast Green Cloud-RAN with Imperfect CSI

Y. Shi, Jun Zhang, K.B. Letaief

Research output: Journal article publicationJournal articleAcademic researchpeer-review

58 Citations (Scopus)


© 1991-2012 IEEE.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 \ell1/\ell2-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 exploiting its convex substructures. In the second stage, we propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as well as, the effectiveness of the proposed algorithm to minimize the network power consumption for multicast Cloud-RAN.
Original languageEnglish
Article number7120176
Pages (from-to)4647-4659
Number of pages13
JournalIEEE Transactions on Signal Processing
Issue number17
Publication statusPublished - 1 Sept 2015
Externally publishedYes


  • alternating optimization
  • Cloud-RAN
  • green communications
  • group-sparsity
  • multicast beamforming
  • PhaseLift
  • robust optimization
  • semidefinite relaxation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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