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    Blind Equalization of IIR Channels Using Hidden Markov Models and Extended Least Squares

    Krishnamurthy, Vikram and Dey, Subhrakanti and LeBlanc, James P (1995) Blind Equalization of IIR Channels Using Hidden Markov Models and Extended Least Squares. IEEE Transactions on Signal Processing, 43 (12). pp. 2994-3006. ISSN 1053-587X

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    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.

    Item Type: Article
    Keywords: Blind Equalization; IIR Channels; Hidden Markov; Models; Extended Least Squares;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 12735
    Identification Number:
    Depositing User: Subhrakanti Dey
    Date Deposited: 14 Apr 2020 10:37
    Journal or Publication Title: IEEE Transactions on Signal Processing
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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