Blind equalization of nonlinear channels using Hidden Markov Models

A novel blind channel equalizer is proposed which is suitable both for linear and nonlinear channels. The proposed equalizer consists of the following three steps: a) identification of the clusters formed by the received data samples, via an unsupervised learning clustering technique, b) labeling of the identified clusters, by using a Hidden Markov Modeling (HMM) of the process and c) channel equalization by means of a Cluster Based Sequence Equalizer. The performance of the equalizer is investigated for a variety of channels (minimum/non-minimum phase, linear/nonlinear channels).