SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems

Authors:
M. Sanchez-Fernandez;M. de-Prado-Cumplido;J. Arenas-Garcia;F. Perez-Cruz
Affiliations:
Dept. Teoria de la Senal y Comunicaciones, Univ. Carlos de Madrid, Leganes-Madrid, Spain;-;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2004

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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.