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

Z. Azmat, H. D. Tuan

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech, and Signal Processing
Subtitle of host publicationICASSP 2013 : proceddings : May 26-31, 2013, Vancouver, British Columbia, Canada
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5765-5769
Number of pages5
ISBN (Print)9781479903566
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventIEEE International Conference on Acoustics, Speech, and Signal Processing (38th : 2013) - Vancouver, Canada
Duration: 26 May 201331 May 2013

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing (38th : 2013)
CityVancouver, Canada
Period26/05/1331/05/13

Keywords

  • Wireless Sensor Networks
  • Gaussian Mixture Models
  • Unscented Transformations

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