Turbo equalization is an iterative equalization and decoding technique that can achieve equally impressive performance gains for communication systems that send digital data over channels that require equalization, i.e., those that suffer from intersymbol interference (ISI). In this article, we discuss the turbo equalization approach to coded data transmission over ISI channels, with emphasis on the basic ideas and some of the practical details. The original system introduced by Douillard et al. can be viewed as an extension of the turbo decoding algorithm by considering the effect of the ISI channel as another form of error protection, i.e., as a rate-1 convolutional code.

Turbo equalization

A functional link artificial neural network for adaptive channel equalization

Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.

Nonlinear channel equalization for QAM signal constellation using artificial neural networks

Equalization refers to any signal processing technique used at the receiver to combat intersymbol interference in dispersive channels. This paper reviews the applications of artificial neural networks (ANNs) in modeling nonlinear phenomenon of channel equalization. The literature associated with different feedforward neural network (NN) based equalizers like multilayer perceptron, functional-link ANN, radial basis function, and its variants are reviewed. Feedback-based NN architectures like recurrent NN equalizers are described. Training algorithms are compared in terms of convergence time and computational complexity for nonlinear channel models. Finally, some limitation of current research activities and further research direction is provided.

Channel Equalization Using Neural Networks: A Review

https://dr.ntu.edu.sg/bitstream/10220/7126/1/36.%20Nonlinear%20channel%20equalization%20for%20wireless%20communication%20systems%20using%20Legendre%20neural%20networks.pdf

Nonlinear channel equalization for wireless communication systems using Legendre neural networks

The paper describes the application of multilayer perceptrons to the problem of adaptive channel equalisation in digital communications systems In particular, the use of decision feedback structures is investigated for time-invariant and time-variant bandlimited channels Simulation results show that the equaliser based on the multilayer perceptron provides better bit error rate performance compared with the conventional decision feedback equaliser if the equaliser length corresponds to the time spread of the channel Increasing the equaliser length leads to equivalent bit error rates for the decision feedback and the multilayer perceptron based equaliser Because of the more complex structure of the multilayer perceptron its adaptive behaviour for time-variant channels is inferior compared with the decision feedback equaliser Nonlinearities on the transmission channel can be equalised much better by the multilayer perceptron based equaliser

M. Meyer

Papers

Multilayer perceptron based decision feedback equalisers for channels with intersymbol interference