Optimal Identical Binary Quantizer Design for Distributed Estimation
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 Crameŕ–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 quantizer—particularly in the moderate to high-SNR regime.
Kar, Swarnendu; Chen, Hao; and Varshney, Pramod K.. (2012). "Optimal Identical Binary Quantizer Design for Distributed Estimation". IEEE Transactions on Signal Processing, 60(7), 3896-3901. http://dx.doi.org/10.1109/TSP.2012.2191777