A low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network

Avon Loy Fernandes, Maxim Raginsky, Todd Coleman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a scheme for rate-constrained distributed non-parametric regression using a wireless sensor network. The scheme is universal across a wide range of sensor noise models, including unbounded and nonadditive noise; it has low complexity, requiring simple operations such as uniform scalar quantization with dither and message passing between neighboring nodes in the network; and attains minimax op-timality for regression functions in common smoothness classes. We present theoretical results on the trade-off between the compression rate and the MSE and demonstrate empirical performance of the scheme using simulations.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages2269-2272
Number of pages4
DOIs
StatePublished - Sep 16 2008
Externally publishedYes
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

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

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/TerritoryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Message-passing algorithms
  • Nonparametrie estimation
  • Rate-distortion theory
  • Sensor networks

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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