Probabilistic tangent subspace: a unified view
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
Fast adaptive digital equalization by recurrent neural networks
IEEE Transactions on Signal Processing
Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters
IEEE Transactions on Fuzzy Systems
A suboptimal approach to channel equalization based on the nearest neighbor rule
IEEE Journal on Selected Areas in Communications
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A new machine learning method called probabilistic tangent subspace is introduced to improve the performance of the equalization for the M-QAM modulation signals in wireless communication systems. Due to the mobility of communicator, wireless communication channels are time variant. The uncertainties in the time-varying channel's coefficients cause the amplitude distortion as well as the phase distortion of the M-QAM modulation signals. On the other hand, the Probabilistic Tangent Subspace method is designed to encode the pattern variations. Therefore, we are motivated to adopt this method to develop a classifier as an equalizer for time-varying channels. Simulation results show that this equalizer performs better than those based on nearest neighbor method and support vector machine method for Rayleigh fading channels.