Blind equalizer for constant-modulus signals based on Gaussian process regression |
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Authors: | Kyuho Hwang Sooyong Choi |
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Affiliation: | School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, 120-749 Seoul, Republic of Korea |
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Abstract: | 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. |
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Keywords: | Gaussian process regression Blind equalization Constant modulus algorithm Machine learning |
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