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Adaptive algorithms for sparse nonlinear channel estimation

Publication ,  Conference
Kalouptsidis, N; Mileounis, G; Babadi, B; Tarokh, V
Published in: IEEE Workshop on Statistical Signal Processing Proceedings
December 25, 2009

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. © 2009 IEEE.

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Published In

IEEE Workshop on Statistical Signal Processing Proceedings

DOI

Publication Date

December 25, 2009

Start / End Page

221 / 224
 

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Kalouptsidis, N., Mileounis, G., Babadi, B., & Tarokh, V. (2009). Adaptive algorithms for sparse nonlinear channel estimation. In IEEE Workshop on Statistical Signal Processing Proceedings (pp. 221–224). https://doi.org/10.1109/SSP.2009.5278600
Kalouptsidis, N., G. Mileounis, B. Babadi, and V. Tarokh. “Adaptive algorithms for sparse nonlinear channel estimation.” In IEEE Workshop on Statistical Signal Processing Proceedings, 221–24, 2009. https://doi.org/10.1109/SSP.2009.5278600.
Kalouptsidis N, Mileounis G, Babadi B, Tarokh V. Adaptive algorithms for sparse nonlinear channel estimation. In: IEEE Workshop on Statistical Signal Processing Proceedings. 2009. p. 221–4.
Kalouptsidis, N., et al. “Adaptive algorithms for sparse nonlinear channel estimation.” IEEE Workshop on Statistical Signal Processing Proceedings, 2009, pp. 221–24. Scopus, doi:10.1109/SSP.2009.5278600.
Kalouptsidis N, Mileounis G, Babadi B, Tarokh V. Adaptive algorithms for sparse nonlinear channel estimation. IEEE Workshop on Statistical Signal Processing Proceedings. 2009. p. 221–224.

Published In

IEEE Workshop on Statistical Signal Processing Proceedings

DOI

Publication Date

December 25, 2009

Start / End Page

221 / 224