The Experts below are selected from a list of 17031 Experts worldwide ranked by ideXlab platform
J.c. Patra - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear Channel Equalization for QAM signal constellation using artificial neural networks
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 1999Co-Authors: J.c. Patra, R. Baliarsingh, G. PandaAbstract: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.
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A functional link artificial neural network for adaptive Channel Equalization
Signal Processing, 1995Co-Authors: J.c. PatraAbstract:Abstract Application of artificial neural network (ANN) structures to the problem of Channel Equalization in a digital communication system has been considered in this paper. The difficulties associated with Channel nonlinearities can be overcome by equalizers employing ANN. Because of nonlinear processing of signals in an ANN, it is capable of producing arbitrarily complex decision regions. For this reason, the ANN has been utilized for the Channel Equalization problem. A scheme based on a functional link ANN (FLANN) has been proposed for this task. The performance of the proposed network along with two other ANN structures has been compared with the conventional LMS based Channel equalizer. Effect of eigenvalue ratio of the input correlation matrix on the performance of the equalizers has been studied. From the simulation results, it is observed that the performance of the proposed FLANN based equalizer outperforms the other two in terms of bit-error rate (BER) and attainable MSB level over a wide range of eigenvalue spread, signal to noise ratio and Channel nonlinearities.
Cenk Toker - One of the best experts on this subject based on the ideXlab platform.
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Joint transceiver design for multiuser MIMO Channel Equalization
2010 IEEE 18th Signal Processing and Communications Applications Conference, 2010Co-Authors: Baris Yuksekkaya, Cenk TokerAbstract:In this paper, joint transmitter receiver filter optimization for Channel Equalization is investigated for a multiuser multiple input multiple output frequency selective slowly varying Channel. Minimum mean square error criterion is used in the Equalization under a total transmit power constraint. Because of the simplicity of the optimization of multiple access Channel, Channel duality is used for the optimization and the solution is transformed to the broadcast, interference, and MIMO Channels.
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VTC Fall - A General Joint Transceiver Design for Multiuser MIMO Channel Equalization
2010 IEEE 72nd Vehicular Technology Conference - Fall, 2010Co-Authors: Baris Yuksekkaya, Cenk TokerAbstract:In this paper, joint transmitter receiver filter optimization for Channel Equalization is investigated for a multiuser multiple input multiple output frequency selective slowly varying Channel. Minimum mean square error criterion is used in the Equalization under a total transmit power constraint. Because of the simplicity of the optimization of multiple access Channel, Channel duality is used for the optimization problem and the solution is transformed to the broadcast, interference and MIMO frequency selective Channels if required.
S A Kassam - One of the best experts on this subject based on the ideXlab platform.
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Channel Equalization using adaptive complex radial basis function networks
IEEE Journal on Selected Areas in Communications, 1995Co-Authors: Inhyok Cha, S A KassamAbstract:It is generally recognized that digital Channel Equalization can be interpreted as a problem of nonlinear classification. Networks capable of approximating nonlinear mappings can be quite useful in such applications. The radial basis function network (RBFN) is one such network. We consider an extension of the RBFN for complex-valued signals (the complex RBFN or CRBFN). We also propose a stochastic-gradient (SG) training algorithm that adapts all free parameters of the network. We then consider the problem of Equalization of complex nonlinear Channels using the CRBFN as part of an equalizer. Results of simulations we have carried out show that the CRBFN with the SG algorithm can be quite effective in Channel Equalization. >
G. Panda - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear Channel Equalization for QAM signal constellation using artificial neural networks
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 1999Co-Authors: J.c. Patra, R. Baliarsingh, G. PandaAbstract: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.
S C Shrivastava - One of the best experts on this subject based on the ideXlab platform.
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Channel Equalization using neural networks a review
Systems Man and Cybernetics, 2010Co-Authors: Kavita Burse, R N Yadav, S C ShrivastavaAbstract: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.
