Applications of neural networks to digital communications: a survey
Signal Processing - Special issue on emerging techniques for communication terminals
Maximum margin equalizers trained with the Adatron algorithm
Signal Processing
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Digital communication receivers using gaussian processes for machine learning
EURASIP Journal on Advances in Signal Processing
Improved super-exponential algorithm for blind equalization
Digital Signal Processing
Analytical method for blind binary signal separation
IEEE Transactions on Signal Processing
Blind equalization of constant modulus signals using support vector machines
IEEE Transactions on Signal Processing
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A new blind equalization method for constant modulus (CM) signals based on Gaussian process for regression (GPR) by incorporating a constant modulus algorithm (CMA)-like error function into the conventional GPR framework is proposed. The GPR framework formulates the posterior density function for weights using Bayes' rule under the assumption of Gaussian prior for weights. The proposed blind GPR equalizer is based on linear-in-weights regression model, which has a form of nonlinear minimum mean-square error solution. Simulation results in linear and nonlinear channels are presented in comparison with the state-of-the-art support vector machine (SVM) and relevance vector machine (RVM) based blind equalizers. The simulation results show that the proposed blind GPR equalizer without cumbersome cross-validation procedures shows the similar performances to the blind SVM and RVM equalizers in terms of intersymbol interference and bit error rate.