Antenna selection in massive MIMO using non-central principal component analysis

M. T. A. Rana*, Rein Vesilo, Iain B. Collings

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Massive MIMO has the potential to offer high throughput in today's fast wireless communication systems, however, the large number of antennas and RF chains needed at the transmitter brings the challenge of high system complexity and hardware energy consumption. In this paper, two semi-heuristic techniques are proposed for practical antenna selection in a multi-user MIMO broadcast system using principal components analysis (PCA) to reduce signal correlations at users and remove antennas that contribute least to system sum capacity, thereby reducing the number of RF chains needed. Zero forcing precoding is used and users are equipped with a single antenna. Using analytic methods PCA eigenvalues are decomposed into two components: The mean channel gain component and the channel correlation component. Using simulation we show that the proposed antenna selection methods perform much better using mean channel gain selection and show how antenna selection depends on the channel matrix structure.

Original languageEnglish
Title of host publicationITNAC 2016
Subtitle of host publication26th International Telecommunication Networks and Applications Conference : proceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)9781509009190
DOIs
Publication statusPublished - 2016
Event26th International Telecommunication Networks and Applications Conference, ITNAC 2016 - Dunedin, New Zealand
Duration: 7 Dec 20169 Dec 2016

Other

Other26th International Telecommunication Networks and Applications Conference, ITNAC 2016
CountryNew Zealand
CityDunedin
Period7/12/169/12/16

Keywords

  • Antenna Selection (AS)
  • Massive MIMO
  • Multi User
  • Principal Component Analysis (PCA)
  • Spectral efficiency (SE)
  • Zero Forcing (ZF)

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