Minimal radial basis function neural networks for nonlinear channel equalisation

The results of linear and nonlinear channel equalisation in data communications are presented, using a recently developed minimal radial basis function neural network structure, referred to as the minimal resource allocation network (MRAN). The MRAN algorithm uses online learning, and has the capability to grow and prune the RBF network's hidden neurons ensuring a parsimonious network structure. Compared to earlier methods, the proposed scheme does not have to estimate the channel order first, and fix the model parameters. Results showing the superior performance of the MRAN algorithm for two linear channels (minimum and nonminimum phase) for 2PAM signalling, and three nonlinear channels for 2PAM and 4QAM signalling, are presented.