NDLI: Achievable Distortion/Rate Tradeoff in a Decentralized Gaussian Parameter Estimation Problem

Content Provider	IEEE Xplore Digital Library
Author	Scaglione, A. Yildiz, M.E. Aysal, T.C.
Copyright Year	2008
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY (Scaglione, A.; Yildiz, M.E.; Aysal, T.C.)
Abstract	In this paper, we study the decentralized version of the classical rate constrained Gaussian parameter estimation problem referred to as the Central Estimation Officer (CEO) problem which we refer to as the Decentralized Estimation Officers (DEO) problem. Like in the CEO case, we consider a group of N sensors observing an independently corrupted version of an infinite i.i.d. sequence of samples from a Gaussian source, in additive Gaussian noise. Unlike the CEO case, the sensors in our study are also the estimation officers. They are uniformly deployed in a circular pattern of radius r and communicate over RF links with limited energy. Their task is to reconstruct the quantity of interest (the samples of the source), without a central fusion node, better than what they are capable of with their local observations. We find achievable scaling laws by structuring our communication protocol as an instance of the so called average consensus algorithm, a gossiping protocol used for averaging original sensor measurements via near neighbors communications. We derive how the Mean Squared Error (MSE) of the sensors' estimation scales with the network size, per node power and ring radius r. Moreover, we compare our results with scaling laws previously derived for the centralized case, i.e, the CEO problem in a comparable scenario.
Starting Page	102
Ending Page	105
File Size	240112
Page Count	4
File Format	PDF
ISBN	9781424416813
DOI	10.1109/IZS.2008.4497286
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-12
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Rate distortion theory Parameter estimation Protocols Wireless sensor networks Sensor fusion Gaussian noise Signal processing Computer architecture Digital communication Seminars parameter estimation CEO problem average consensus sensor networks
Content Type	Text
Resource Type	Article

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