Fractionally-spaced blind channel equalisation using hidden Markov models

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