FPGA realization of a radial basis function based nonlinear channel equalizer

Poyueh Chen, Hungming Tsai, Cheng Jian Lin, Chiyung Lee

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


In this paper we propose a radial basis function (RBF) neural network for nonlinear time-invariant channel equalizer. The RBF network model has a three-layer structure which is comprised of an input layer, a hidden layer and an output layer. The learning algorithm consists of unsupervised learning and supervised learning. The unsupervised learning mainly adjusts the weight among input layer and hidden layer. The supervised learning adjusts the weight among output layer and hidden layer. We will implement RBF by using FPGA. Computer simulation results show that the bit error rates of the RBF equalize using software and hardware implements are close to that of the optimal equalizer.

Original languageEnglish
Pages (from-to)320-325
Number of pages6
JournalLecture Notes in Computer Science
Issue numberIII
Publication statusPublished - 2005
EventSecond International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
Duration: 2005 May 302005 Jun 1

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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