Power allocation for Gaussian Mixture model prior knowledge in wirless sensor networks

Azmat, Z; Tuan, HD

Power allocation for Gaussian Mixture model prior knowledge in wirless sensor networks

Azmat, Z Tuan, HD

Permalink

Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2013, pp. 5765 - 5769
Issue Date:: 2013-10-18

Closed Access

	Filename	Description	Size
	2013004173OK.pdf		171.99 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Azmat, Z	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.date.issued	2013-10-18	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2013, pp. 5765 - 5769	en_US
dc.identifier.isbn	9781479903566	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28422
dc.description.abstract	This paper presents power allocation in nonlinear sensor networks for Gaussian Mixture (GM) information source. The observations of sensors are transmitted through independent Rayleigh flat fading channels to a fusion centre (FC). Transmit Power is optimally allocated to sensor nodes so as to minimize the mean square error (MSE) of estimate at FC. Bayesian linear and optimal nonlinear estimators are deployed at FC to compare the proposed optimal and uniform power allocation among sensors. Extensive simulations validate that the proposed Bayesian linear estimator with optimized power gains effectively works for GM prior distribution. © 2013 IEEE.	en_US
dc.relation.ispartof	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICASSP.2013.6638769	en_US
dc.title	Power allocation for Gaussian Mixture model prior knowledge in wirless sensor networks	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Vancouver, Canada	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents power allocation in nonlinear sensor networks for Gaussian Mixture (GM) information source. The observations of sensors are transmitted through independent Rayleigh flat fading channels to a fusion centre (FC). Transmit Power is optimally allocated to sensor nodes so as to minimize the mean square error (MSE) of estimate at FC. Bayesian linear and optimal nonlinear estimators are deployed at FC to compare the proposed optimal and uniform power allocation among sensors. Extensive simulations validate that the proposed Bayesian linear estimator with optimized power gains effectively works for GM prior distribution. © 2013 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/28422