Blind equalization of IIR channels using hidden Markov models and extended least squares

TitleBlind equalization of IIR channels using hidden Markov models and extended least squares
Publication TypeJournal Article
Year of Publication1995
AuthorsKrishnamurthy, V., S. Dey, and J. P. LeBlanc
JournalSignal Processing, IEEE Transactions on
Pagination2994 -3006
Date Publisheddec.
Keywordsblind equalization algorithm, channel noise variance, computational complexity, constant modulus algorithm, equalisers, extended least squares, finite state Markov chain, hidden Markov models, IIR channels, IIR filters, least squares approximations, noisy channels, parameter estimation, probability, recursive estimation, recursive estimator, SNR, state estimation, strictly positive real scheme, telecommunication channels, transition probabilities, truncated FIR scheme

In this paper, we present a blind equalization algorithm for noisy IIR channels when the channel input is a finite state Markov chain. The algorithm yields estimates of the IIR channel coefficients, channel noise variance, transition probabilities, and state of the Markov chain. Unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length, our algorithm is computationally inexpensive. Our algorithm is based on combining a recursive hidden Markov model (HMM) estimator with a relaxed SPR (strictly positive real) extended least squares (ELS) scheme. In simulation studies we show that the algorithm yields satisfactory estimates even in low SNR. We also compare the performance of our scheme with a truncated FIR scheme and the constant modulus algorithm (CMA) which is currently a popular algorithm in blind equalization


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