Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
563642 | Signal Processing | 2011 | 10 Pages |
Abstract
In this paper, identification of sparse linear and nonlinear systems is considered via compressive sensing methods. Efficient algorithms are developed based on Kalman filtering and Expectation-Maximization. The proposed algorithms are applied to linear and nonlinear channels which are represented by sparse Volterra models and incorporate the effect of power amplifiers. Simulation studies confirm significant performance gains in comparison to conventional non-sparse methods.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Nicholas Kalouptsidis, Gerasimos Mileounis, Behtash Babadi, Vahid Tarokh,