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

Avon L. Fernandes, Maxim Raginsky, Todd P. Coleman

Research output: Contribution to journalArticle

Abstract

We propose a scheme for rate-constrained distributed nonparametric 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 optimality for regression functions in common smoothness classes. We present theoretical results on the tradeoff between the compression rate, communication complexity of encoding, and the MSE and demonstrate empirical performance of the scheme using simulations.

Original languageEnglish (US)
Pages (from-to)1731-1744
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume57
Issue number5
DOIs
StatePublished - May 25 2009

Keywords

  • Conditional rate-distortion theory
  • Distributed estimation
  • Distributed sequential entropy coding
  • Dithered scalar quantization
  • Low-complexity schemes
  • Minimax-optimal estimators
  • Nonparametric regression
  • Sensor networks
  • Universal orthogonal series estimators

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network'. Together they form a unique fingerprint.

  • Cite this