Recursive Bayesian algorithms for blind equalization

A novel blind equalization algorithm based on a suboptimum Bayesian symbol sequence estimator is presented. It is shown that a parallel bank of Kalman filters can be used to update a suboptimum Bayesian formula for the sequence possibilities. Two methods are used to reduce the computational complexity of the algorithm. First, it is shown that the Kalman filters can be replaced by simpler least-mean-square (LMS) adaptive filters. Second, the technique of reduced-state sequence estimation is adopted to reduce the number of symbol subsequences considered in the Bayesian updating, and hence the number of parallel filters required. The performance properties of the resulting algorithms are evaluated through bit error simulations, and these are compared to the bounds of optimum maximum-likelihood sequence estimation. It is shown that the Kalman filter and LMS-based algorithms achieve blind start-up and rapid convergence (within 200 iterations) for both binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation formats