PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems
Matilde Fernandez-Sanchez, Mario Prado-Cumplido, Jeronimo Arenas-Garcia and Fernando Perez-Cruz
IEEE Transactions on Signal Processing Volume 52, Number 8, pp. 2298-2307, 2004. ISSN 1053-587X

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:523
Deposited By:Fernando Perez-Cruz
Deposited On:24 December 2004