A CLUSTERING TECHNIQUE FOR DIGITAL-COMMUNICATIONS CHANNEL EQUALIZATION USING RADIAL BASIS FUNCTION NETWORKS

The paper investigates the application of a radial basis function network to digital communications channel equalization. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equalization solution can be achieved efficiently using a simple and robust supervised clustering algorithm. During data transmission a decision-directed version of the clustering algorithm enables the radial basis function network to track a slowly time-varying environment. Moreover, the clustering scheme provides an automatic compensation for nonlinear channel and equipment distortion. This represents a radically new approach to the adaptive equalizer design. Computer simulations are included to illustrate the analytical results.