Adaptive algorithms for sparse nonlinear channel estimation

Author

Kalouptsidis, Nicholas ; Mileounis, Gerasimos ; Babadi, Behtash ; Tarokh, Vahid

Author_Institution

Dept. of Inf. & Telecommun., Univ. of Athens, Athens, Greece

fYear

2009

Firstpage

221

Lastpage

224

Abstract

In this paper, we consider the estimation of sparse nonlinear communication channels. Transmission over the channels is represented by sparse Volterra models that incorporate the effect of Power Amplifiers. Channel estimation is performed by compressive sensing methods. Efficient algorithms are proposed based on Kalman filtering and Expectation Maximization. Simulation studies confirm that the proposed algorithms achieve significant performance gains in comparison to the conventional non-sparse methods.

Keywords

Kalman filters; Volterra equations; channel estimation; expectation-maximisation algorithm; power amplifiers; Kalman filtering; adaptive algorithms; compressive sensing; expectation maximization; power amplifiers; sparse Volterra models; sparse nonlinear channel estimation; sparse nonlinear communication channels; Adaptive algorithm; Adaptive estimation; Channel estimation; Communication channels; Filtering algorithms; Kalman filters; Power amplifiers; Power system modeling; Repeaters; Satellites; Adaptive estimation; Compressive sensing; Expectation Maximization; Kalman filtering; Volterra series;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on

Conference_Location

Cardiff

Print_ISBN

978-1-4244-2709-3

Electronic_ISBN

978-1-4244-2711-6

Type

conf

DOI

10.1109/SSP.2009.5278600

Filename

5278600