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Adaptive nonlinear equalizer using a mixture of Gaussian-based online density estimator

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posted on 30.06.2017, 12:07 by Hao Chen, Yu Gong, Xia Hong, Sheng Chen
This paper introduces a new adaptive nonlinear equalizer relying on a radial basis function (RBF) model, which is designed based on the minimum bit error rate (MBER) criterion, in the system setting of the intersymbol interference channel plus cochannel interference (CCI). Our proposed algorithm is referred to as the online mixture of Gaussian-estimator-aided MBER (OMG-MBER) equalizer. Specifically, a mixture of Gaussian-based probability density function (pdf) estimator is used to model the pdf of the decision variable, for which a novel online pdf update algorithm is derived to track the incoming data. With the aid of this novel online mixture of Gaussian-based sample-by-sample updated pdf estimator, our adaptive nonlinear equalizer is capable of updating its equalizer's parameters sample by sample to aim directly at minimizing the RBF nonlinear equalizer's achievable bit error rate (BER). The proposed OMG-MBER equalizer significantly outperforms the existing online nonlinear MBER equalizer, known as the least bit error rate equalizer, in terms of both the convergence speed and the achievable BER, as is confirmed in our simulation study.

Funding

This work was supported in part by the U.K. Engineering and Physical Sciences Research Council and in part by the Defence Science and Technology Laboratory under Grant EP/H012516/1.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Vehicular Technology

Volume

63

Issue

9

Pages

4265 - 4276

Citation

CHEN, H. ... et al, 2014. Adaptive nonlinear equalizer using a mixture of Gaussian-based online density estimator. IEEE Transactions on Vehicular Technology, 63 (9), pp. 4265-4276.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2014

Notes

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

ISSN

0018-9545

eISSN

1939-9359

Language

en