Title

Asymptotic Optimal Quantizer Design for Distributed Bayesian Estimation

Document Type

Conference Proceeding

Publication Date

2016

Abstract

In this paper, we address the optimal quantizer design problem for distributed Bayesian parameter estimation with one-bit quantization at local sensors. A performance limit obtained for any distributed parameter estimator with a known prior is adopted as a guidance for quantizer design. Aided by the performance limit, the optimal quantizer and a set of noisy observation models that achieve the performance limit are derived. Further, when the performance limit may not be achievable for some applications, we develop a nearoptimal estimator which consists of a dithered noise and a single threshold quantizer. In the scenario where the parameter is Gaussian and signal-to-noise ratio is greater than −1.138 dB, we show that one can construct such an estimator that achieves approximately 99.65% of the performance limit.