A clustering technique for digital communications channel equalization using radial basis function networks

TLDR

Radial basis function networks are applied to digital communications channel equalization. A simple supervised clustering algorithm trains the network to implement the Bayesian equalizer, and a decision‑directed version tracks slow channel variations, as demonstrated by simulations. The network matches the optimal Bayesian symbol‑decision equalizer and automatically compensates for nonlinear channel and equipment distortion.

Abstract

The application of a radial basis function network to digital communications channel equalization is examined. 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. Computer simulations are included to illustrate the analytical results.