Optimal rate allocation for the vector Gaussian CEO problem

Jin Jun Xiao, Zhi Quan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Consider the problem of estimating a vector source with a bandwidth constrained sensor network in which sensors make distributed observations on the source and collaborate with a fusion center (FC) to generate a final estimate. Due to power and band-width limitations, each sensor must compress its data and transmit to the FC only the minimum amount of information necessary to ensure the final estimate meets a given distortion bound. The optimal power allocation for the class of linear decentralized analog compression schemes was considered in [6] and proved to be NP-hard in general. In this paper, we consider the optimal rate allocation problem in the so called Berger-Tung achievable rate distortion region. In contrast to the power allocation for the linear analog compression schemes, we show that the optimal rate allocation can be formulated as a convex optimization problem which can be efficiently solved by interior point methods. Our convex reformulation technique is also applicable to the vector Gaussian multiterminal source coding problem.

Original languageEnglish (US)
Title of host publicationIEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Pages56-59
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventIEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Puerto Vallarta, Mexico
Duration: Dec 13 2005Dec 15 2005

Publication series

NameIEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Volume2005

Other

OtherIEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
CountryMexico
CityPuerto Vallarta
Period12/13/0512/15/05

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