Paper Info
Title
SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems
Abstract
This paper addresses the problem of multiple-input multiple-output (MIMO) frequency nonselective channel estimation. We develop a new method for multiple variable regression estimation based on Support Vector Machines (SVMs): a state-of-the-art technique within the machine learning community for regression estimation. We show how this new method, which we call M-SVR, can be efficiently applied. The proposed regression method is evaluated in a MIMO system under a channel estimation scenario, showing its benefits in comparison to previous proposals when nonlinearities are present in either the transmitter or the receiver sides of the MIMO system.
Year
DOI
Venue
2004
10.1109/TSP.2004.831028
IEEE Transactions on Signal Processing
Keywords
Field
DocType
MIMO systems,channel estimation,computational complexity,error statistics,learning (artificial intelligence),nonlinear systems,regression analysis,support vector machines,telecommunication computing,SVM multiregression estimation,bit error rate,computational complexity,machine learning,multiple-input multiple-output systems,nonlinear channel estimation,support vector machines,Channel estimation,MIMO systems,multivariate regression,support vector machine
Transmitter,Control theory,Regression analysis,Support vector machine,Communication channel,MIMO,Estimation theory,System identification,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
52
8
1053-587X
Citations 
PageRank 
References 
66
2.38
22
Authors
4
Name
Order
Citations
PageRank
Sanchez-Fernandez, M.1662.38
de-Prado-Cumplido, M.2662.38
Arenas-Garcia, J.31398.68
Perez-Cruz, F.4745.00