A novel approach to data-efficient blind channel identification and equalization

This paper introduces a new approach to blind identification and equalization of communication channels. This approach exploits the finite alphabet (FA) constraint on the input symbol sequence and the algebraic structures of the symbol, channel output and channel parameter matrices. A forward-backward optimization method is also introduced to exploit the structural information. Initial simulations show that the proposed approach can achieve relatively good BER performance when compared with the Viterbi-based maximum likelihood sequence estimator (VB-MLSD) using known sampled channel impulse response