Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach

Yuanming Shi, Jun Zhang, Wei Chen, Khaled B. Letaief

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks, which, however, suffers high computational complexity. In particular, a complex optimization problem needs to be solved to obtain the remote radio head (RRH) ordering criterion in each transmission block, which will help to determine the active RRHs and the associated fronthaul links. In this paper, we propose innovative approaches to reduce the complexity of this key step in group sparse beamforming. Specifically, we first develop a smoothed \ell -{p} -minimization approach with the iterative reweighted- \ell -{2} algorithm to return a Karush-Kuhn-Tucker (KKT) point solution, as well as enhance the capability of inducing group sparsity in the beamforming vectors. By leveraging the Lagrangian duality theory, we obtain closed-form solutions at each iteration to reduce the computational complexity. The well-structured solutions provide opportunities to apply the large-dimensional random matrix theory to derive deterministic approximations for the RRH ordering criterion. Such an approach helps to guide the RRH selection only based on the statistical channel state information, which does not require frequent update, thereby significantly reducing the computation overhead. Simulation results shall demonstrate the performance gains of the proposed \ell -{p} -minimization approach, as well as the effectiveness of the large system analysis-based framework for computing the RRH ordering criterion.

Original languageEnglish
Pages (from-to)2511-2524
Number of pages14
JournalIEEE Transactions on Wireless Communications
Issue number4
Publication statusPublished - 1 Apr 2018
Externally publishedYes


  • Cloud-RAN
  • green communications
  • Lagrangian duality
  • random matrix theory
  • smoothed lp-minimization
  • sparse optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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