Minimal antenna-subset selection under capacity constraint for power-efficient MIMO systems: A relaxed ℓ1 minimization approach

Masahiro Yukawa, Isao Yamada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper addresses the minimal subset selection of antennas achieving designated channel capacity. This is one of the most natural approaches to alleviating the power consumption in MIMO systems, while it is a mathematically challenging nonlinearly-constrained sparse optimization (ℓ0-norm minimization) problem. We present an efficient algorithmic solution, to this highly combinatorial problem, using convex and differentiable relaxations of the ℓ0-norm. The proposed algorithm is based on the hybrid steepest descent method for the subgradient projection operator together with the soft-thresholding technique, minimizing the Moreau envelope of the ℓ1-norm subject to the capacity constraint. The simulation results show that the proposed algorithm realizes a near optimal solution to the original nonlinearly-constrained sparse optimization problem.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3058-3061
Number of pages4
ISBN (Print)9781424442966
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 2010 Mar 142010 Mar 19

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period10/3/1410/3/19

Keywords

  • Antenna selection
  • Convex optimization
  • MIMO systems

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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