NDLI: Blind identification/equalization using deterministic maximum likelihood and a partial information on the input

Content Provider	IEEE Xplore Digital Library
Author	Alberge, F. Duhamel, P. Nikolova, M.
Copyright Year	1999
Description	Author affiliation: ENST, Paris, France (Alberge, F.)
Abstract	A new algorithm for jointly estimating the channels and the symbols sent through these channels is presented. Although the symbols are assumed to belong to a finite set, we use this prior information only partially. The rationale is that under some precise circumstances the plain deterministic maximum likelihood method seldom, if ever, exhibits local minima, while a full use of the finite alphabet property is more efficient, but introduces numerous local minima. The use of a partial information allows to considerably improve the performance in terms of symbol estimation without adding a new local minimum. Our algorithm combines a least-squares estimation of the channels and a constrained minimisation of a quadratic criterion for the symbols. If the data are noise-free, it is shown that the global minimum is attained only for the true filter and symbols. Furthermore, me propose a growing window technique which permits to evaluate whether the actual solution is a global minimum or not. In the second case, our technique permits to escape from this local minimum. Numerical simulations illustrate the accuracy of our algorithm in the presence of noise.
Starting Page	259
Ending Page	262
File Size	420251
Page Count	4
File Format	PDF
ISBN	0780355997
DOI	10.1109/SPAWC.1999.783068
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-05-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Blind equalizers Maximum likelihood estimation Iterative algorithms Wireless communication Minimization methods Digital signal processing Filters Numerical simulation Image restoration Speech
Content Type	Text
Resource Type	Article

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