Paper Info
Title
Probabilistic tangent subspace method for M-QAM signal equalization in time-varying multipath channels
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.
Year
DOI
Venue
2005
10.1007/11538356_98
ICIC (2)
Keywords
Field
DocType
multipath channel,m-qam modulation signal,wireless communication channel,probabilistic tangent subspace method,amplitude distortion,m-qam signal equalization,support vector machine method,new machine,time-varying channel,phase distortion,wireless communication system,nearest neighbor method,machine learning,wireless communication,support vector machine
Rayleigh fading,Equalization (audio),Computer science,Phase distortion,Artificial intelligence,Probabilistic logic,Amplitude distortion,Quadrature amplitude modulation,Pattern recognition,Subspace topology,Algorithm,Speech recognition,Amplitude modulation
Conference
Volume
ISSN
ISBN
3645
0302-9743
3-540-28227-0
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Jing Yang100.68
Yunpeng Xu2214.44
Hongxing Zou313913.17