Distributed estimation in Gaussian noise for bandwidth-constrained wireless sensor networks

We study deterministic mean-location parameter estimation when only quantized versions of the original observations are available, due to bandwidth constraints. When the dynamic range of the parameter is small or comparable with the noise variance, we introduce a class of maximum likelihood estimators that require transmitting just one bit per sensor to achieve an estimation variance close to that of the (clairvoyant) sample mean estimator. When the dynamic range is comparable or large relative to the noise standard deviation, we show that an optimum quantization step exists to achieve the best possible variance for a given bandwidth constraint. We also establish that in certain cases the sample mean estimator formed by quantized observations is preferable for complexity reasons. We finally address implementation issues and guarantee that all the numerical maximizations required by the proposed estimators are concave.