PERFORMANCE EVALUATION OF SELF-ORGANIZING MAP BASED NEURAL EQUALIZERS IN DYNAMIC DISCRETE-SIGNAL DETECTION

Novel equalizer structures utilizing neural computation have recently been developed for adaptive discrete-signal detection. The equalizer structures combine the traditional transversal equalizer and the Self-Organizing Map algorithm in parallel or cascade. Extensive simulations have been run to investigate different parameter effects using a two-path channel model and 16-QAM modulation. The results have shown that the neural equalizer adapts very well to changing channel conditions, including both linear multipath and nonlinear distortions. Especially in difficult channels, the new structures are superior when compared with the traditional equalizers. The computational complexities of the combined structures are not significantly higher when compared to the practical linear equalizers.