Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yang, J., Xu, Y., Zou, H. (2005). Probabilistic Tangent Subspace Method for M-QAM Signal Equalization in Time-Varying Multipath Channels. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_98
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DOI: https://doi.org/10.1007/11538356_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28227-3
Online ISBN: 978-3-540-31907-8
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