Channel complexity reduction in massive MISO using principal component analysis

M. T. A. Rana, Rein Vesilo

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

2 Citations (Scopus)

Abstract

Massive multiple-input-multiple-output (MIMO) has the potential to offer a high throughput in today's fast wireless communication systems, however the large number of antennas at the transmitter brings the challenge of high channel complexity and hardware energy consumption. In this paper channel the complexity in massive MISO systems is reduced with a negligible loss of sum-capacity by using Principal Component Analysis (PCA). Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) precoding schemes are used and users are equipped with a single antenna. The results are simulated using MATLAB using the Rayleigh fading channel model. Numerical results verify that, the channel complexity in terms of floating-point operations (FLOPs) has been reduced by more than 80% by using the proposed technique.

Original languageEnglish
Title of host publication2017 17th International Symposium on Communications and Information Technologies (ISCIT)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)9781509065141
ISBN (Print)9781509065158
DOIs
Publication statusPublished - 2017
Event17th International Symposium on Communications and Information Technologies, ISCIT 2017 - Cairns, Australia
Duration: 25 Sep 201727 Sep 2017

Conference

Conference17th International Symposium on Communications and Information Technologies, ISCIT 2017
CountryAustralia
CityCairns
Period25/09/1727/09/17

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  • Cite this

    Rana, M. T. A., & Vesilo, R. (2017). Channel complexity reduction in massive MISO using principal component analysis. In 2017 17th International Symposium on Communications and Information Technologies (ISCIT) (pp. 1-6). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ISCIT.2017.8261192