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A parallel reduced-complexity filtering algorithm for sub-optimal Kalman per-survivor processing

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2 Author(s)
Rollins, M.E. ; Dept. of Electr. & Comput. Eng., Queen''s Univ., Kingston, Ont., Canada ; Simmons, S.J.

Per-survivor processing (PSP) [Raheli et al., 1991] has been proposed for overcoming the difficulties of conventional decision-directed channel estimation for maximum likelihood sequence estimation (MLSE) in rapidly varying channels. Optimal PSP requires elaborate statistical channel modelling and complex Kalman filtering techniques. In the present paper, a novel filtering algorithm is presented for sub-optimal Kalman PSP. Simplification of the channel ARMA model and the introduction of a prediction-feedback mechanism into the Kalman recursions lead to a reduced-complexity filtering algorithm with parallel structure. Computer simulations are used to evaluate the performance of the technique in an MLSE decoder for frequency-selective Rayleigh fading channels. Results indicate that for an O(N2) reduction in algorithm complexity only a small increase in error rate occurs

Published in:

Global Telecommunications Conference, 1994. Communications Theory Mini-Conference Record, 1994 IEEE GLOBECOM., IEEE

Date of Conference:

28 Nov- 2 Dec 1994