Adaptive Algorithms for Sparse Nonlinear Channel Estimation

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Adaptive Algorithms for Sparse Nonlinear Channel Estimation

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Title: Adaptive Algorithms for Sparse Nonlinear Channel Estimation
Author: Tarokh, Vahid; Babadi, Behtash; Mileounis, Gerasimos; Kalouptsidis, Nicholas

Note: Order does not necessarily reflect citation order of authors.

Citation: Kalouptsidis, Nicholas, Gerasimos Mileounis, Behtash Babadi, and Vahid Tarokh. 2009. Adaptive algorithms for sparse nonlinear channel estimation. Paper presented at the 2009 IEEE Workshop on Statistical Signal Processing, Cardiff, Wales, UK.
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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.
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4481494

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  • FAS Scholarly Articles [7078]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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