Optimal Channel-aware Bayesian Estimation with 1-bit Quantization

Santosh Paudel, Hao Chen

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

Abstract

We address the optimal quantizer design problem for distributed Bayesian parameter estimation where each sensor quantizes its local observation into one bit and transmit it through non-ideal channels to the Fusion Center. We first develop an asymptotic performance limit (PL) as a performance bound for any quantizer design with a known prior. Aided by this PL, we derive the optimal quantizer and near optimal quantizer with set of observation models that achieves the PL, thus solve a set of optimal quantizer design problem for distributed estimation.

Original languageEnglish
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages752-756
Number of pages5
ISBN (Electronic)9781665458283
DOIs
StatePublished - 2021
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: 31 Oct 20213 Nov 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2021-October
ISSN (Print)1058-6393

Conference

Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove
Period31/10/213/11/21

Keywords

  • Asymptotic Performance Limit
  • Cramer-Rao Lower Bound
  • Distributed Bayesian Estimation
  • Non-ideal Channel
  • One-bit Quantization

Fingerprint

Dive into the research topics of 'Optimal Channel-aware Bayesian Estimation with 1-bit Quantization'. Together they form a unique fingerprint.

Cite this