A study of hyperplane-based vector quantization for distributed estimation

Jun Fang, Hongbin Li

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

2 Scopus citations

Abstract

We consider the problem of distributed estimation of a vector parameter in wireless sensor networks (WSNs). Due to stringent power and bandwidth constraints, vector quantization is performed at each sensor to convert its local noisy vector observation into one bit of information. The one bit quantized data is then sent to the fusion center (FC), where a final estimate of the vector parameter is formed. The vector quantization problem is studied in such a distributed estimation context. Specifically, our study focuses on a class of hyperplane-based vector quantizers which linearly convert the observation vector into a scalar by using a compression vector and then carry out a scalar quantization. Under the framework of the Cramér-Rao bound (CRB) analysis, we study the choice of the quantization thresholds and the design of the compression vectors.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages2898-2901
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 14 Mar 201019 Mar 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period14/03/1019/03/10

Keywords

  • Distributed estimation
  • Hyperplane-based vector quantization
  • Wireless sensor network (WSN)

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