Channel complexity reduction in massive MISO using principal component analysis

M. T. A. Rana; Rein Vesilo

doi:10.1109/ISCIT.2017.8261192

Channel complexity reduction in massive MISO using principal component analysis

M. T. A. Rana, Rein Vesilo

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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 language	English
Title of host publication	2017 17th International Symposium on Communications and Information Technologies (ISCIT)
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781509065141
ISBN (Print)	9781509065158
DOIs	https://doi.org/10.1109/ISCIT.2017.8261192
Publication status	Published - 2017
Event	17th International Symposium on Communications and Information Technologies, ISCIT 2017 - Cairns, Australia Duration: 25 Sept 2017 → 27 Sept 2017

Conference

Conference	17th International Symposium on Communications and Information Technologies, ISCIT 2017
Country/Territory	Australia
City	Cairns
Period	25/09/17 → 27/09/17

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10.1109/ISCIT.2017.8261192

Cite this

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title = "Channel complexity reduction in massive MISO using principal component analysis",

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.",

author = "Rana, {M. T. A.} and Rein Vesilo",

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doi = "10.1109/ISCIT.2017.8261192",

language = "English",

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note = "17th International Symposium on Communications and Information Technologies, ISCIT 2017 ; Conference date: 25-09-2017 Through 27-09-2017",

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Rana, MTA & Vesilo, R 2017, Channel complexity reduction in massive MISO using principal component analysis. in 2017 17th International Symposium on Communications and Information Technologies (ISCIT). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1-6, 17th International Symposium on Communications and Information Technologies, ISCIT 2017, Cairns, Australia, 25/09/17. https://doi.org/10.1109/ISCIT.2017.8261192

Channel complexity reduction in massive MISO using principal component analysis. / Rana, M. T. A.; Vesilo, Rein.
2017 17th International Symposium on Communications and Information Technologies (ISCIT). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Channel complexity reduction in massive MISO using principal component analysis

AU - Rana, M. T. A.

AU - Vesilo, Rein

PY - 2017

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N2 - 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.

AB - 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.

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DO - 10.1109/ISCIT.2017.8261192

M3 - Conference proceeding contribution

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EP - 6

BT - 2017 17th International Symposium on Communications and Information Technologies (ISCIT)

PB - Institute of Electrical and Electronics Engineers (IEEE)

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T2 - 17th International Symposium on Communications and Information Technologies, ISCIT 2017

Y2 - 25 September 2017 through 27 September 2017

ER -

Channel complexity reduction in massive MISO using principal component analysis

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