Neuro Bayesian blind equalization with BER estimation in digital channels

The implementation of an optimal Bayesian algorithm for digital equalization is infeasible due to its computational complexity. We present a new approach to Bayesian blind equalization which is based on a hybrid architecture involving neural networks and evolutionary computation concepts. We develop a theoretical analysis which leads to recursive formulas to estimate the probability density functions (PDFs) of both the channel and the received samples. These parameters are used directly by the algorithms to perform equalization. Beginning with a revision of previous neural, genetic and radial basis function (RBF) networks-based approaches, we outline the theoretical algorithm that will serve as a reference for future neuro-evolutionary derivations. We also show the capability of these structures to perform blind bit error rate (BER) estimation in reception. Finally, several computer simulations and comparative results are exposed.