Adaptive algorithms for sparse system identification

Authors:
Nicholas Kalouptsidis;Gerasimos Mileounis;Behtash Babadi;Vahid Tarokh
Affiliations:
Department of Informatics and Telecommunications, University of Athens, Panepistimiopolis, 157 84 Ilissia, Athens, Greece;Department of Informatics and Telecommunications, University of Athens, Panepistimiopolis, 157 84 Ilissia, Athens, Greece;School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA;School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Venue:
Signal Processing
Year:
2011

Citing 15
Cited 4

General structure of adaptive algorithms: adaptation and tracking

Adaptive system identification and signal processing algorithms
Atomic Decomposition by Basis Pursuit

SIAM Journal on Scientific Computing
Principles of Digital Transmission: With Wireless Applications

Principles of Digital Transmission: With Wireless Applications
Adaptive Filters

Adaptive Filters
Sparse LMS for system identification

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
RLS-weighted Lasso for adaptive estimation of sparse signals

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images

SIAM Review
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

SIAM Journal on Imaging Sciences
Comparison of SPARLS and RLS algorithms for adaptive filtering

SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
Iterative and sequential algorithms for multisensor signalenhancement

IEEE Transactions on Signal Processing
Efficient algorithms for Volterra system identification

IEEE Transactions on Signal Processing
Adaptive filtering

Automatica (Journal of IFAC)
Just relax: convex programming methods for identifying sparse signals in noise

IEEE Transactions on Information Theory
An EM algorithm for wavelet-based image restoration

IEEE Transactions on Image Processing

Estimating second-order Volterra system parameters from noisy measurements based on an LMS variant or an errors-in-variables method

Signal Processing
Adaptive matching pursuit using coordinate descent and double residual minimization

Signal Processing
Compressed sensing techniques for receiver based post-compensation of transmitter's nonlinear distortions in OFDM systems

Signal Processing
On the stochastic modeling of the IAF-PNLMS algorithm for complex and real correlated Gaussian input data

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

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.