Linear precoding for distributed estimation of correlated sources in WSN MIMO system

Ajib S. Arifin; Tomoaki Ohtsuki

doi:10.1109/VTCSpring.2013.6692616

Linear precoding for distributed estimation of correlated sources in WSN MIMO system

Ajib S. Arifin, Tomoaki Ohtsuki

Department of Information and Computer Science

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

4 Citations (Scopus)

Abstract

We consider distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean squared error (LMMSE) fusion rule that accommodates correlated sources. We obtain a closed-form solution that follows water-filling strategy. We also derive a lower bound distortion to this model. Simulation results show that when the sources are more correlated, the distortion in terms of mean squared error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.

Original language	English
Title of host publication	2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013 - Proceedings
DOIs	https://doi.org/10.1109/VTCSpring.2013.6692616
Publication status	Published - 2013 Dec 1
Event	2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013 - Dresden, Germany Duration: 2013 Jun 2 → 2013 Jun 5

Publication series

Name	IEEE Vehicular Technology Conference
ISSN (Print)	1550-2252

Other

Other	2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013
Country/Territory	Germany
City	Dresden
Period	13/6/2 → 13/6/5

Keywords

Distributed estimation
MIMO
Power constraint
Spatial correlated data
Water-filling
Wireless sensor network

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1109/VTCSpring.2013.6692616

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Arifin, AS & Ohtsuki, T 2013, Linear precoding for distributed estimation of correlated sources in WSN MIMO system. in 2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013 - Proceedings., 6692616, IEEE Vehicular Technology Conference, 2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013, Dresden, Germany, 13/6/2. https://doi.org/10.1109/VTCSpring.2013.6692616

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title = "Linear precoding for distributed estimation of correlated sources in WSN MIMO system",

abstract = "We consider distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean squared error (LMMSE) fusion rule that accommodates correlated sources. We obtain a closed-form solution that follows water-filling strategy. We also derive a lower bound distortion to this model. Simulation results show that when the sources are more correlated, the distortion in terms of mean squared error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.",

keywords = "Distributed estimation, MIMO, Power constraint, Spatial correlated data, Water-filling, Wireless sensor network",

author = "Arifin, {Ajib S.} and Tomoaki Ohtsuki",

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doi = "10.1109/VTCSpring.2013.6692616",

language = "English",

isbn = "9781467363372",

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note = "2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013 ; Conference date: 02-06-2013 Through 05-06-2013",

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AU - Arifin, Ajib S.

AU - Ohtsuki, Tomoaki

PY - 2013/12/1

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N2 - We consider distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean squared error (LMMSE) fusion rule that accommodates correlated sources. We obtain a closed-form solution that follows water-filling strategy. We also derive a lower bound distortion to this model. Simulation results show that when the sources are more correlated, the distortion in terms of mean squared error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.

AB - We consider distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean squared error (LMMSE) fusion rule that accommodates correlated sources. We obtain a closed-form solution that follows water-filling strategy. We also derive a lower bound distortion to this model. Simulation results show that when the sources are more correlated, the distortion in terms of mean squared error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.

KW - Distributed estimation

KW - MIMO

KW - Power constraint

KW - Spatial correlated data

KW - Water-filling

KW - Wireless sensor network

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M3 - Conference contribution

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T3 - IEEE Vehicular Technology Conference

BT - 2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013 - Proceedings

T2 - 2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013

Y2 - 2 June 2013 through 5 June 2013

ER -

Linear precoding for distributed estimation of correlated sources in WSN MIMO system

Abstract

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Keywords

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