Title :
Blind equalization of IIR channels using hidden Markov models and extended least squares
Author :
Krishnamurthy, Vikram ; Dey, Subhrakanti ; LeBlanc, James P.
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
fDate :
12/1/1995 12:00:00 AM
Abstract :
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
Keywords :
IIR filters; computational complexity; equalisers; hidden Markov models; least squares approximations; parameter estimation; probability; recursive estimation; state estimation; telecommunication channels; IIR channels; SNR; blind equalization algorithm; channel noise variance; constant modulus algorithm; extended least squares; finite state Markov chain; hidden Markov models; noisy channels; recursive estimator; strictly positive real scheme; transition probabilities; truncated FIR scheme; Blind equalizers; Finite impulse response filter; Hidden Markov models; IIR filters; Least squares methods; Maximum likelihood estimation; Probability density function; Recursive estimation; State estimation; Viterbi algorithm;
Journal_Title :
Signal Processing, IEEE Transactions on