DocumentCode :
3066917
Title :
Optimum estimation via partition functions and information measures
Author :
Merhav, Neri
Author_Institution :
Dept. of Electr. Eng., Technion - Israel Inst. of Technol., Haifa, Israel
fYear :
2010
fDate :
13-18 June 2010
Firstpage :
1473
Lastpage :
1477
Abstract :
In continuation to a recent work on the statistical-mechanical analysis of optimum estimation in Gaussian noise via its relation to the mutual information (I-MMSE relation), here we propose a more direct relation between optimum estimation and some information measures, which can be viewed as partition functions and hence are amenable to statistical-mechanical analysis. This approach has several advantages, most notably, its applicability to general sources/channels, as opposed to the I-MMSE relation and its variants which hold only for certain classes of channels. We also demonstrate the derivation of the optimum estimator and the MMSE in a few examples. One of them is generalizable to a fairly wide class of sources and channels. For this class, our approach yields an approximate conditional mean estimator and an MMSE formula that has the flavor of a single-letter expression.
Keywords :
Gaussian noise; channel estimation; least mean squares methods; statistical analysis; Gaussian noise optimum estimation; I-MMSE relation; approximate conditional mean estimator; channel estimator; partition functions; single-letter expression; statistical-mechanical analysis; AWGN; Cities and towns; Covariance matrix; Electric variables measurement; Gaussian noise; Information analysis; Mechanical variables measurement; Mutual information; Noise measurement; Physics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Theory Proceedings (ISIT), 2010 IEEE International Symposium on
Conference_Location :
Austin, TX
Print_ISBN :
978-1-4244-7890-3
Electronic_ISBN :
978-1-4244-7891-0
Type :
conf
DOI :
10.1109/ISIT.2010.5513588
Filename :
5513588
Link To Document :
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