Sparseness-Aware Data-Selective LMS Algorithm

Data-selective adaptive algorithms are well-suited for reducing the complexity of weight updating in system identification problems. Nevertheless, impulse noise can obstruct the effectiveness of their data selection schemes. To address this issue, we introduce a sparsity-aware data-selective least m...

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Bibliographic Details
Published inIEEE International Conference on Consumer Electronics-China (Online) pp. 789 - 790
Main Authors Chien, Ying-Ren, Hsieh, Han-En
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.07.2023
Subjects
Adaptive algorithms
Complexity theory
Data-selective least mean square (DS-LMS)
Estimation
impulse noise (IN)
Robustness
Signal processing
Signal processing algorithms
sparseness
System identification
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ISSN2575-8284
DOI10.1109/ICCE-Taiwan58799.2023.10226731

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Summary:Data-selective adaptive algorithms are well-suited for reducing the complexity of weight updating in system identification problems. Nevertheless, impulse noise can obstruct the effectiveness of their data selection schemes. To address this issue, we introduce a sparsity-aware data-selective least mean square (DS-LMS) algorithm that enhances the data selection scheme for sparse system identification in the presence of impulse noise. Our approach was tested through numerical experiments, which confirmed its efficacy.
ISSN:2575-8284
DOI:10.1109/ICCE-Taiwan58799.2023.10226731