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Title: Adaptive nonlinear equalizer using a mixture of Gaussian-based online density estimator
Authors: Chen, Hao
Gong, Yu
Hong, Xia
Chen, Sheng
Keywords: Adaptive nonlinear equalizer
Minimum bit error rate (MBER)
Mixture of Gaussians
Probability density function (pdf)
Radial basis function (RBF)
Issue Date: 2014
Publisher: © IEEE
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.
Abstract: 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.
Description: © 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.
Sponsor: 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.
Version: Accepted for publication
DOI: 10.1109/TVT.2014.2313458
URI: https://dspace.lboro.ac.uk/2134/25656
Publisher Link: http://dx.doi.org/10.1109/TVT.2014.2313458
ISSN: 0018-9545
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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