@inproceedings{cc5a1d36bcdb4807b7897319473ddb94,
title = "Multi-bit Quantizer Design for Distributed Parameter Estimation",
abstract = "We consider sensors deployed in diverse locations measuring a common parameter through noisy observations. These observations are quantized to be sent to a fusion center doing the estimation of the common parameter. We design these quantizers to minimize the worst-case mean square error for common parameter estimation. Relying on an asymptotic regime in terms of sensors' number and on random multi-bit quantizer per sensor, we provide a relevant continuous distribution for the thresholds of these quantizers via signomial programming. Through numerical simulations, we show that the proposed quantizers outperform the uniformly-distributed one and some deterministic ones even when the number of sensors is limited.",
keywords = "Cramer-Rao bound, Distributed estimation, Minimax, Quantization, Signomial programming",
author = "Yue Bi and Philippe Ciblat and Yue Wu and Cunqing Hua",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE Statistical Signal Processing Workshop, SSP 2025 ; Conference date: 08-06-2025 Through 11-06-2025",
year = "2025",
month = jan,
day = "1",
doi = "10.1109/SSP64130.2025.11073223",
language = "English",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
booktitle = "2025 IEEE Statistical Signal Processing Workshop, SSP 2025",
}