Maximum likelihood decoding of QAM signals in Markov modulated fading channels

TitleMaximum likelihood decoding of QAM signals in Markov modulated fading channels
Publication TypeConference Paper
Year of Publication1997
AuthorsCollings, I. B., A. Logothetis, and V. Krishnamurthy
Conference NameSignal Processing Advances in Wireless Communications, 1997 1st IEEE Signal Processing Workshop on
Pagination405 -408
Date Publishedapr.
Keywordsautoregressive processes, Bayes methods, cellular radio, channel estimates, city environments, decoder, digital signal estimates, estimation theory, expectation maximization algorithm, fading, fading statistics, Kalman filter, Kalman filters, land mobile radio, large area coverage mobile cells, Markov chain, Markov modulated fading channels, Markov processes, maximum likelihood decoding, maximum likelihood estimation, optimal batch algorithm, optimality, optimisation, QAM signals, quadrature amplitude modulation, quadrature-amplitude-modulated signals, sub-optimal on-line algorithm, time varying AR process, time-varying channels, Viterbi algorithm, Viterbi decoding

This paper presents a new decoder for quadrature-amplitude-modulated (QAM) signals transmitted through Markov modulated fading channels. Markov modulated channels arise in city environments when a direct line of sight is blocked intermittently, and for large area coverage mobile cells where the channel has large variations in fading statistics. The channel is modelled as a time varying AR process, with coefficients which depend on a Markov chain. An optimal batch algorithm is presented which provides estimates of the channel and the digital signal. This is achieved by optimally coupling a Viterbi algorithm and a Kalman filter. The optimality is demonstrated via the expectation maximization algorithm. In addition, a sub-optimal on-line algorithm has been derived. By considering the channel to be Markov modulated, benefits can be gained over standard techniques which assume fixed fading statistics


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