Robust Blind Learning Algorithm for Nonlinear Equalization.

NONLINEAR EQUALIZER DESIGN: In order to mitigate the time-varying nonlinear effects of the power-line channel, it is necessary to design a nonlinear equalizer. A self-improved nonlinear equalizer using a multilayer perceptron is proposed in this paper. This equalization gives a good compromise among the system reliability, the algorithm.

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Performance Evaluation of Nonlinear Equalizer 931 considered. This will result in a multipath scenario with frequency selective effect. The rest of this paper is organized as follows. In Section II, a model of an OFDM-based PLC system is presented (5)-(6). Section III contains Linear Equalizer Design.

Abstract. Recently a new blind equalization method was proposed for the 16QAM constellation input inspired by the maximum entropy density approximation technique with improved equalization performance compared to the maximum entropy approach, Godard’s algorithm, and others.

In this paper, we propose a new blind learning algorithm, namely, the Benveniste-Goursat input-output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new.

This paper proposes a novel algorithm based on minimizing mutual information for a special case of nonlinear blind source separation: post-nonlinear blind source separation. A network composed of a set of radial basis function (RBF) networks, a set of multilayer perceptron and a linear network is used as a demixing system to separate sources in post-nonlinear mixtures. The experimental results.

In particular, MFCM successively estimates the channel output states with relatively high speed and substantial accuracy. Therefore, the Bayesian equalizer based on MFCM can constitute a viable solution for various problems of nonlinear blind channel equalization. Our future research pursuits are oriented towards the use of the MFCM under more.

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This paper presents two new blind learning algorithms to achieve robust convergence for linear or nonlinear equalization. Rather than only using the output information contained in equalizer's output signals, the input decision information involved in the input signals is employed to assist the blind learning procedure. Based on this input information, two blind algorithms, Benveniste-Goursat.

He led a research team at BT for 10 years on both theoretical and experimental investigations in ultra-high speed optical systems from 1981 to 1991. He jointly established the photonics research group at Aston University 1991-2000 specialising in soliton communication and processing. During this time the research was extensively funded by EPSRC.

Our goal in this paper is to design the equalizer that reconstructs the transmitted sequence x. The design utilizes only the channel observations y, i.e., without knowing the channel parameters, including the impulse response h, the nonlinear function g() and the noise variance. This is an unsupervised blind channel equalization problem where pilot signals are not available. We will discuss.

Reduction of Nonlinear Inter-Subcarrier Intermixing in Coherent Optical OFDM by a Fast Newton-based Support Vector Machine Nonlinear Equalizer.