Hyper Radial Basis Function Neural Networks for Interference Cancellation with Nonlinear Processing of Reference Signal

Efficient interference cancellation often requires nonlinear processing of a reference signal. In this paper, hyper radial basis function (HRBF) neural networks for adaptive interference cancellation is developed. We show that the HRBF networks, with an appropriate learning algorithm, is able to approximate the interference signal more efficiently than standard radial basis function (RBF) networks. The HRBF network-based canceller achieves better results for interference cancellation. This is due to the capabilities of the HRBF networks to approximate arbitrary multidimensional nonlinear functions and better flexibility in comparison to RBF networks. Simulation examples and comparisons to the FIR-based linear canceller and the RBFN-based canceller demonstrate the usefulness and effectiveness of the HRBFN based canceller.  2001 Academic Press