LMS algorithms for tracking slow Markov chains with applications to hidden Markov estimation and adaptive multiuser detection

TitleLMS algorithms for tracking slow Markov chains with applications to hidden Markov estimation and adaptive multiuser detection
Publication TypeJournal Article
Year of Publication2005
AuthorsYin, G. G., and V. Krishnamurthy
JournalIEEE Transactions on Information Theory
Volume51
Pagination2475–2490
ISSN0018-9448
Abstract

This paper analyzes the tracking properties of the least mean squares (LMS) algorithm when the underlying parameter evolves according to a finite-state Markov chain with infrequent jumps. First, using perturbed Liapunov function methods, mean-square error estimates are obtained for the tracking error. Then using recent results on two-time-scale Markov chains, mean ordinary differential equation and diffusion approximation results are obtained. It is shown that a sequence of the centered tracking errors converges to an ordinary differential equation. Moreover, a suitably scaled sequence of the tracking errors converges weakly to a diffusion process. It is also shown that iterate averaging of the tracking algorithm results in optimal asymptotic convergence rate in an appropriate sense. Two application examples, analysis of the performance of an adaptive multiuser detection algorithm in a direct-sequence code-division multiple-access (DS/CDMA) system, and tracking analysis of the state of a hidden Markov model (HMM) with infrequent jumps, are presented.

URLhttp://dx.doi.org/10.1109/TIT.2005.850075
DOI10.1109/TIT.2005.850075

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