Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Linear neural network based blind equalization
Signal Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Robustness to fractionally-spaced equalizer length using theconstant modulus criterion
IEEE Transactions on Signal Processing
Dithered signed-error CMA: robust, computationally efficient blindadaptive equalization
IEEE Transactions on Signal Processing
Fast adaptive digital equalization by recurrent neural networks
IEEE Transactions on Signal Processing
Nonlinear blind equalization schemes using complex-valued multilayer feedforward neural networks
IEEE Transactions on Neural Networks
Channel equalization using neural networks: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The blind equalizers based on complex valued feedforward neural networks, for linear and nonlinear communication channels, yield better performance as compared to linear equalizers. The learning algorithms are, generally, based on stochastic gradient descent, as they are simple to implement. However, these algorithms show a slow convergence rate. In the blind equalization problem, the unavailability of the desired output signal and the presence of nonlinear activation functions make the application of recursive least squares algorithm difficult. In this letter, a new scheme using recursive least squares algorithm is proposed for blind equalization. The learning of weights of the output layer is obtained by using a modified version of constant modulus algorithm cost function. For the learning of weights of hidden layer neuron space adaptation approach is used. The proposed scheme results in faster convergence of the equalizer.