Fractionally-spaced blind channel equalisation using hidden Markov models

TitleFractionally-spaced blind channel equalisation using hidden Markov models
Publication TypeConference Paper
Year of Publication1997
AuthorsKrishnamurthy, V., and K. Dogancay
Conference NameAcoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Pagination3929 -3932 vol.5
Date Publishedapr.
Keywordsaggregation, channel characteristics, channel input sequence, channel input symbol rate, channel input symbols estimation, channel output sequence, computational complexity, computational complexity reduction, continuous-time channel output, EM algorithm, equalisers, expectation-maximisation algorithm, finite-state Markov chain, fractionally spaced blind channel equalisation, hidden Markov models, maximum likelihood algorithm, maximum likelihood estimation, multichannel outputs, oversampling, performance, reduced cost blind equalisation algorithm, reliability, sequences, signal sampling, simulation, stochastic complementation, stochastic processes, telecommunication channels, training data
Abstract

The paper presents a maximum likelihood (ML) blind channel equalisation algorithm based on the expectation-maximisation (EM) algorithm. We assume that the channel input sequence is a finite-state Markov chain and the channel output sequence is obtained from the continuous-time channel output by oversampling it at a rate higher than the channel input symbol rate, which leads to a fractionally-spaced channel equalisation problem. The objective of blind channel equalisation is to estimate the channel input symbols without explicit knowledge of the channel characteristics and the requirement of training data. The availability of multichannel outputs for the same channel input improves the reliability of the estimates. A reduced-cost blind equalisation algorithm which draws on aggregation by stochastic complementation is also proposed. A simulation example is presented to demonstrate the performance of the proposed algorithms

URLhttp://dx.doi.org/10.1109/ICASSP.1997.604771
DOI10.1109/ICASSP.1997.604771

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