Distributed estimation and detection with bounded transmissions over gaussian multiple access channels

Sivaraman Dasarathan, Cihan Tepedelenlioglu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel is considered. When the sensor measurements are decreasingly reliable as a function of the sensor index, the conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variances of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify-and-forward technique and its robustness to impulsive sensing noise distributions is highlighted. In contrast to amplify-and-forward schemes, it is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results.

Original languageEnglish (US)
Article number6823697
Pages (from-to)3454-3463
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume62
Issue number13
DOIs
StatePublished - Jul 1 2014

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Keywords

  • Asymptotic variance
  • bounded transmissions
  • deflection coefficient
  • distributed detection
  • distributed estimation
  • multiple access channel

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

Cite this

Distributed estimation and detection with bounded transmissions over gaussian multiple access channels. / Dasarathan, Sivaraman; Tepedelenlioglu, Cihan.

In: IEEE Transactions on Signal Processing, Vol. 62, No. 13, 6823697, 01.07.2014, p. 3454-3463.

Research output: Contribution to journalArticle

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