Compressive sampling and adaptive multipath estimation

Date
2010
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Source Title
2010 IEEE 18th Signal Processing and Communications Applications Conference
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Publisher
IEEE
Volume
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Pages
260 - 263
Language
Turkish
Type
Conference Paper
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Abstract

In many signal processing problems such as channel estimation and equalization, the problem reduces to a linear system of equations. In this proceeding we formulate and investigate linear equations systems with sparse perturbations on the coefficient matrix. In a large class of matrices, it is possible to recover the unknowns exactly even if all the data, including the coefficient matrix and observation vector is corrupted. For this aim, we propose an optimization problem and derive its convex relaxation. The numerical results agree with the previous theoretical findings of the authors. The technique is applied to adaptive multipath estimation in cognitive radios and a significant performance improvement is obtained. The fact that rapidly varying channels are sparse in delay and doppler domain enables our technique to maintain reliable communication even far from the channel training intervals. ©2010 IEEE.

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Keywords
Compressed sensing, Matrix identification, Sparse multipath channels, Structured perturbations, Structured total least squares, Compressed sensing, Matrix identification, Sparse multi-path channel, Structured perturbations, Structured total least squares, Estimation, Linear systems, Multipath propagation, Relaxation processes, Signal processing, Signal reconstruction, Matrix algebra
Citation
Published Version (Please cite this version)