Interference cancellation using radial basis function networks

In this paper we investigate radial basis function networks (RBFNs) for application in adaptive interference cancellation. We study the problem from the perspective of optimal signal estimation. Optimum interference cancellation usually requires nonlinear processing of signals. RBFNs, owing to their nonlinear function approximation capability, can be expected to be able to implement or approximate the operation of optimum interference cancellation with appropriate network configuration and training. We examine a number of different RBFN structures as well as training algorithms. In particular, we show that in some special cases the optimum interference canceler can be exactly implemented by a class of normalized RBFNs that we have recently proposed. Through a number of simulation examples, we demonstrate that the various neural networks based on radial basis functions can be very useful for interference cancellation problems in which traditional linear cancelers may fail badly. We also discuss issues related to network performance and learning algorithms, and some practical considerations.