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

Abstract

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… (More)
DOI: 10.1006/dspr.2001.0398

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