Adaptive Algorithms for Sparse Nonlinear Channel Estimation

DSpace/Manakin Repository

Adaptive Algorithms for Sparse Nonlinear Channel Estimation

Show simple item record

dc.contributor.author Tarokh, Vahid
dc.contributor.author Babadi, Behtash
dc.contributor.author Mileounis, Gerasimos
dc.contributor.author Kalouptsidis, Nicholas
dc.date.accessioned 2010-10-14T14:14:36Z
dc.date.issued 2009
dc.identifier.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. en
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4481494
dc.description.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. en
dc.description.sponsorship Engineering and Applied Sciences en
dc.language.iso en_US en
dash.license OAP
dc.title Adaptive Algorithms for Sparse Nonlinear Channel Estimation en
dash.depositing.author Tarokh, Vahid
dc.date.available 2010-10-14T14:14:36Z

Files in this item

Files Size Format View
ssp-54-70202-final.pdf 247.7Kb PDF View/Open

This item appears in the following Collection(s)

  • FAS Scholarly Articles [7289]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

Show simple item record

 
 

Search DASH


Advanced Search
 
 

Submitters