SVM Multiregression for Nonlinear Channel Estimation in Multiple-Input Multiple-Output Systems
IEEE Transactions on Signal ProcessingPublished 20 July 2004
Matilde Sánchez-Fernández, Mario DePrado‐Cumplido, Jerónimo Arenas‐García, Fernando Pérez‐Cruz
Citations274
SJR quartileQ1
SJR score2.05
SNIP2.36
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TL;DR
This paper develops a new method for multiple variable regression estimation based on Support Vector Machines: a state-of-the-art technique within the machine learning community for regression estimation, and shows how this new method can be efficiently applied.
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.
Keywords
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