论文信息 - Reduced computation blind equalization for FIR channel input Markov models

Reduced computation blind equalization for FIR channel input Markov models

The paper deals with adaptive blind equalization for the transmission of digital modulated signals on an unknown dispersive channel with additive white gaussian noise. The observed signal being modeled as a hidden Markov model(HMM), the expectation-maximization (EM) algorithm can be used to realize both the channel identification and the estimation of the emitted symbols. The paper proposes a new on-line algorithm with reduced complexity. This algorithm, obtained as an approximate solution of the maximum likelihood (ML) problem via the EM sequential algorithm, has strong connections with decision feedback equalizers (DFE) using the recursive least square (RLS) Algorithm.

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