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Wireless Power Transfer for Distributed Estimation in Sensor Networks
KTH, School of Electrical Engineering (EES), Automatic Control. Dankook Univ, South Korea.
2017 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 11, no 3, 549-562 p.Article in journal (Refereed) Published
Abstract [en]

This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency-based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the nonconvex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for collocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 11, no 3, 549-562 p.
Keyword [en]
Amplify-and-forwarding, best linear unbiased estimator (BLUE), distributed estimation, mean-squared error (MSE), wireless power transfer (WPT)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-208265DOI: 10.1109/JSTSP.2017.2678106ISI: 000399674500009Scopus ID: 2-s2.0-85018515386OAI: oai:DiVA.org:kth-208265DiVA: diva2:1113872
Note

QC 20170622

Available from: 2017-06-22 Created: 2017-06-22 Last updated: 2017-06-22Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf