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Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm

Citation Author(s):
Abdullah Alshabilli, Shihab Jimaa
Submitted by:
Luis Weruaga
Last updated:
19 March 2016 - 12:32pm
Document Type:
Poster
Document Year:
2016
Event:
Presenters Name:
Luis Weruaga

Abstract 

Abstract: 

Embedding the l1 norm in gradient-based adaptive filtering is a popular solution for sparse plant estimation. Supported on the modal analysis of the adaptive algorithm near steady state, this work shows that the optimal sparsity tradeoff depends on filter length, plant sparsity and signal-to-noise ratio. In a practical implementation, these terms are obtained with an unsupervised mechanism tracking the filter weights. Simulation results prove the robustness and superiority of the novel adaptive-tradeoff sparsity-aware method.

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