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

Yuanming Shi, Jun Zhang, Wei Chen, Khaled Letaief

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks (Cloud-RANs), 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 <formula> <tex>$&#x2113;_{p}$</tex> </formula>-minimization approach with the iterative reweighted-<formula> <tex>$&#x2113;_{2}$</tex> </formula> algorithm to return a Karush-Kuhn-Tucker (KKT) point solution, as well as enhancing 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 the 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 (CSI), which does not require frequent update, thereby significantly reducing the computation overhead. Simulation results shall demonstrate the performance gains of the proposed <formula> <tex>$&#x2113;_{p}$</tex> </formula>-minimization approach, as well as the effectiveness of the large system analysis based framework for computing RRH ordering criterion.

Original languageEnglish
JournalIEEE Transactions on Wireless Communications
DOIs
Publication statusAccepted/In press - 6 Feb 2018

Fingerprint

Beamforming
Random Matrices
Computational Complexity
Lagrangian Duality
Duality Theory
Random Matrix Theory
Channel State Information
Computational complexity
Systems Analysis
Closed-form Solution
Sparsity
Power Consumption
Update
Optimization Problem
Channel state information
Minimise
Iteration
Computing
Approximation
Electric power utilization

Keywords

  • Algorithm design and analysis
  • Array signal processing
  • Cloud computing
  • Cloud-RAN
  • green communications
  • Lagrangian duality
  • Power demand
  • random matrix theory
  • Signal processing algorithms
  • smoothed &#x2113;p-minimization
  • sparse optimization
  • Wireless networks

ASJC Scopus subject areas

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

Cite this

Enhanced Group Sparse Beamforming for Green Cloud-RAN : A Random Matrix Approach. / Shi, Yuanming; Zhang, Jun; Chen, Wei; Letaief, Khaled.

In: IEEE Transactions on Wireless Communications, 06.02.2018.

Research output: Contribution to journalArticle

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