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arXiv · 1803.11096

Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS

Danqi Jin·Jie Chen·Cedric Richard·Jingdong Chen
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Abstract

Group zero-attracting LMS and its reweighted form have been proposed for addressing system identification problems with structural group sparsity in the parameters to estimate. Both algorithms however suffer from a trade-off between sparsity degree and estimation bias and, in addition, between convergence speed and steady-state performance like most adaptive filtering algorithms. It is therefore necessary to properly set their step size and regularization parameter. Based on a model of their transient behavior, we introduce a variable-parameter variant of both algorithms to address this issue. By minimizing their mean-square deviation at each time instant, we obtain closed-form expressions of the optimal step size and regularization parameter. Simulation results illustrate the effectiveness of the proposed algorithms.

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Danqi Jin, Jie Chen, Cedric Richard, Jingdong Chen. 2018-03-29. Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS. https://arxiv.org/abs/1803.11096

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