Hyper radial basis function neural networks for interference cancellation with nonlinear processing of reference signal

Sergiy A. Vorobyov, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

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 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 fiexibility 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.

Original languageEnglish
Pages (from-to)204-221
Number of pages18
JournalDigital Signal Processing: A Review Journal
Volume11
Issue number3
DOIs
Publication statusPublished - Jul 2001
Externally publishedYes

Keywords

  • Green's functions
  • Hyper radial basis functions
  • Interference and noise cancellation
  • Learning
  • Manhattan algorithm
  • Neural networks
  • Nonlinear mapping

Fingerprint

Dive into the research topics of 'Hyper radial basis function neural networks for interference cancellation with nonlinear processing of reference signal'. Together they form a unique fingerprint.

Cite this