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Contribution to Book
Asymptotic Optimal Quantizer Design for Distributed Bayesian Estimation
2016 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings
  • Xia Li, Boise State University
  • Jun Guo, Boise State University
  • Uri Rogers, Eastern Washington University
  • Hao Chen, Boise State University
Document Type
Conference Proceeding
Publication Date
1-1-2016
DOI
http://dx.doi.org/10.1109/ICASSP.2016.7472370
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.

Citation Information
Xia Li, Jun Guo, Uri Rogers and Hao Chen. "Asymptotic Optimal Quantizer Design for Distributed Bayesian Estimation" 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings (2016)
Available at: http://works.bepress.com/hao_chen/38/