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A Bayesian maximum-likelihood sequence estimation algorithm for a priori unknown channels and symbol timing

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1 Author(s)
Iltis, R.A. ; Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA

It is shown that the optimum demodulator for the case of an a priori unknown channel and symbol timing can be approximated using a modified Viterbi algorithm (VA), in which the branch metrics are obtained from the conditional innovations of a bank of extended Kalman filters (EKFs). Each EKF computes channel and timing estimates conditioned on one of the survivor sequences in the trellis. It is also shown that the minimum-variance channel and timing estimates can be approximated by a sum of conditional EKF estimates, weighted by the VA metrics. Simulated bit error rate (BER) results and averaged-squared channel/timing error trajectories are presented, with estimation errors compared to the Cramer-Rao lower bound. The BER performance of the modified VA is also shown to be superior to that obtained using a decision-directed channel/timing estimation algorithm

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Selected Areas in Communications, IEEE Journal on  (Volume:10 ,  Issue: 3 )