Optimal identical binary quantizer design for distributed estimation

Swarnendu Kar, Hao Chen, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


We consider the design of identical one-bit probabilistic quantizers for distributed estimation in sensor networks. We assume the parameter-range to be finite and known and use the maximum Crame-Rao lower bound (CRB) over the parameter-range as our performance metric. We restrict our theoretical analysis to the class of antisymmetric quantizers and determine a set of conditions for which the probabilistic quantizer function is greatly simplified. We identify a broad class of noise distributions, which includes Gaussian noise in the low-SNR regime, for which the often used threshold-quantizer is found to be minimax-optimal. Aided with theoretical results, we formulate an optimization problem to obtain the optimum minimax-CRB quantizer. For a wide range of noise distributions, we demonstrate the superior performance of the new quantizerparticularly in the moderate to high-SNR regime.

Original languageEnglish (US)
Article number6174480
Pages (from-to)3896-3901
Number of pages6
JournalIEEE Transactions on Signal Processing
Issue number7
StatePublished - Jul 2012


  • Distributed estimation
  • dithering
  • minimax CRLB
  • probabilistic quantization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Optimal identical binary quantizer design for distributed estimation'. Together they form a unique fingerprint.

Cite this