Equalization techniques using a simplified bilinear recursive polynomial perceptron with decision feedback

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)

Abstract

This paper presents nonlinear blind equalization techniques using an adaptive bilinear polynomial filter. Two types of blind adaptive bilinear polynomial equalizers with reduced bilinear terms are proposed. One is a blind bilinear polynomial decision feedback equalizer using conventional constant modulus algorithm, which uses previous detected symbols. The other is a blind predictive constant modulus bilinear decision feedback equalizer, which uses error signals. In proposed equalizers, the input of the bilinear section is composed of the feedback inputs multiplied by not overall but middle parts of feedforward inputs. It can be seen by various simulations that proposed simplified blind bilinear equalizers perform better than the conventional blind decision feedback equalizer (DFE) and has almost the same performance as the conventional DFE using training sequences in a digital communication system.

Original languageEnglish
Pages2883-2888
Number of pages6
Publication statusPublished - 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: 2001 Jul 152001 Jul 19

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'01)
CountryUnited States
CityWashington, DC
Period01/7/1501/7/19

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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