A neuro-evolutionary framework for Bayesian blind equalization in digital communications

摘要

The application of the Bayesian formulation to the joint data and channel estimation in digital communication is not feasible in practice because the computational complexity and memory requirements of the estimation process grow exponentially with time. However, the evolution with time of the channel conditional density model suggests the application of pruning, selection, crossover and other concepts from evolutionary computation and neural networks, which drastically reduce the complexity of the Bayesian equalizer without severe performance degradation. Although some problems of convergence to wrong channel estimates may arise, Bayesian equalizers can detect those situations by estimating, during operation, the overall symbol error probability. If suboptimal convergence is detected, the estimation process is automatically re-started.