Normalized LMS adaptive filter algorithm: principles and verilog HDL implementation

Abstract This article offers a detailed exploration of the Normalized Least Mean Squares (NLMS) algorithm, highlighting its enhanced adaptive filtering capabilities and robust performance in managing input signal correlations. It delves into the foundational principles of the NLMS algorithm and outlines the essential modules required for its implementation in Verilog HDL. The implementation and simulation of the NLMS adaptive filter are studied. The simulation outcomes indicate that the convergence rate of the algorithm is superior to that of the conventional LMS algorithm.

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Publication Details

Journal
Journal of Physics Conference Series
Published
2025-04-01
DOI
https://doi.org/10.1088/1742-6596/2991/1/012021
Citations
1
Primary Topic
Advanced Adaptive Filtering Techniques
Type
article
Field-Weighted Citation Impact
1.33
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article

Normalized LMS adaptive filter algorithm: principles and verilog HDL implementation

Dezhen Yang, Peiyuan Wan, Ming Liu, Xu Liu et al.
1 citations
Journal of Physics Conference Series
Advanced Adaptive Filtering Techniques
1.33
article

Normalized LMS adaptive filter algorithm: principles and verilog HDL implementation

Dezhen Yang, Peiyuan Wan, Ming Liu, Xu Liu, Bingjie Li, Zhijie Chen, Haoxu Wang
article en
1 citations

Abstract

Abstract This article offers a detailed exploration of the Normalized Least Mean Squares (NLMS) algorithm, highlighting its enhanced adaptive filtering capabilities and robust performance in managing input signal correlations. It delves into the foundational principles of the NLMS algorithm and outlines the essential modules required for its implementation in Verilog HDL. The implementation and simulation of the NLMS adaptive filter are studied. The simulation outcomes indicate that the convergence rate of the algorithm is superior to that of the conventional LMS algorithm.

Journal of Physics Conference SeriesVol. 2991(1)
Beijing University of Technology (CN)
Openalex Percentile: Top 25%
Advanced Adaptive Filtering Techniques
1.33
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