MAXIMUM-LIKELIHOOD BLIND EQUALIZATION

A new approach to blind equalization is investigated in which the receiver performs joint data and channel estimation in an iterative manner. Instead of estimating the channel inverse, the receiver computes the maximum-likelihood estimate of the channel itself. The iterative algorithm that is developed involves maximum-likelihood sequence estimation (Viterbi decoding) for the data estimation part, and least-squares 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.