Stability analysis of the bias compensated LMS algorithm

Pimenta, Rodrigo M.S.; Petraglia, Mariane R.; Haddad, Diego B.

doi:10.1016/j.dsp.2024.104395

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Cited by 3 publications

References 53 publications

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Multitask diffusion adaptation over hyper-networks

Bahraini,

Shamsollahi,

Afkhaminia

2025

Digital Signal Processing

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Multitask diffusion adaptation over hyper-networks

Bahraini,

Shamsollahi,

Afkhaminia

2025

Digital Signal Processing

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Adaptive Denoising Study Based on Improved Variable Step Size LMS Algorithm

Zhang,

Li,

Cheng

et al. 2024

2024 Second International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE)

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Robust Bias Compensation Method for Sparse Normalized Quasi-Newton Least-Mean with Variable Mixing-Norm Adaptive Filtering

Chien,

Hsieh,

Qian

2024

Mathematics

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Input noise causes inescapable bias to the weight vectors of the adaptive filters during the adaptation processes. Moreover, the impulse noise at the output of the unknown systems can prevent bias compensation from converging. This paper presents a robust bias compensation method for a sparse normalized quasi-Newton least-mean (BC-SNQNLM) adaptive filtering algorithm to address these issues. We have mathematically derived the biased-compensation terms in an impulse noisy environment. Inspired by the convex combination of adaptive filters’ step sizes, we propose a novel variable mixing-norm method, BC-SNQNLM-VMN, to accelerate the convergence of our BC-SNQNLM algorithm. Simulation results confirm that the proposed method significantly outperforms other comparative works regarding normalized mean-squared deviation (NMSD) in the steady state.

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Stability analysis of the bias compensated LMS algorithm

Cited by 3 publications

References 53 publications

Multitask diffusion adaptation over hyper-networks

Multitask diffusion adaptation over hyper-networks

Adaptive Denoising Study Based on Improved Variable Step Size LMS Algorithm

Robust Bias Compensation Method for Sparse Normalized Quasi-Newton Least-Mean with Variable Mixing-Norm Adaptive Filtering

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