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Proceedings Paper

Maximum-likelihood blind equalization
Author(s): Monisha Ghosh; Charles L. Weber
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Paper Abstract

A new approach to blind equalization is investigated in which the receiver performs joint data and channel estimation in an iterative manner. Hence, instead of estimating the channel inverse, the receiver computes the maximum likelihood estimate of the channel itself. The iterative algorithm involves maximum likelihood sequence estimation (Viterbi decoding) for the data estimation part and least squared estimation for the channel estimation part. A suboptimal algorithm is also proposed that uses a reduced-state trellis instead of the Viterbi algorithm. Simulation results show that the performance obtained by these algorithms is comparable to that of a receiver operating with complete knowledge of the channel.

Paper Details

Date Published: 1 December 1991
PDF: 8 pages
Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); doi: 10.1117/12.49776
Show Author Affiliations
Monisha Ghosh, Univ. of Southern California (United States)
Charles L. Weber, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 1565:
Adaptive Signal Processing
Simon Haykin, Editor(s)

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