Bayesian algorithms for blind equalization using parallel adaptive filtering
Abstract
A new blind equalization algorithm based on a suboptimum Bayesian symbolbysymbol detector is presented. It is first shown that the maximum a posteriori (MAP) sequence probabilities can be approximated using the innovations likelihoods generated by a parallel bank of Kalman filters. These filters generate a set of channel estimates conditioned on the possible symbol subsequences contributing to the intersymbol interference. The conditional estimates and MAP symbol metrics are then combined using a suboptimum Bayesian formula. Two methods are considered to reduce the computational complexity of the algorithm. First, the technique of reducedstate sequence estimation is adopted to reduce the number of symbol subsequences considered in the channel estimation process and hence the number of parallel filters required.
 Publication:

IEEE Transactions on Communications
 Pub Date:
 February 1994
 Bibcode:
 1994ITCom..42.1017I
 Keywords:

 Adaptive Control;
 Adaptive Filters;
 Algorithms;
 Bayes Theorem;
 Kalman Filters;
 Parallel Processing (Computers);
 Probability Theory;
 Channels (Data Transmission);
 Estimates;
 Intersymbolic Interference;
 Phase Shift Keying;
 Electronics and Electrical Engineering