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conference paper
Sparse diffusion LMS for distributed adaptive estimation
2012
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to improve the performance of the diffusion strategies. We provide convergence and performance analysis of the proposed method, showing under what conditions it outperforms the unregularized diffusion version. Simulation results illustrate the advantage of the proposed filter under the sparsity assumption on the true coefficient vector.
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Type
conference paper
Authors
Publication date
2012
Publisher
Published in
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Start page
3281
End page
3284
Peer reviewed
REVIEWED
EPFL units
Event name | Event place | Event date |
Kyoto, Japan | March 25-30, 2012 | |
Available on Infoscience
December 19, 2017