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2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
Although the LMS algorithm is often preferred in practice due to its numerous positive implementation properties, once the parameter space to estimate becomes large, the algorithm suffers of slow learning. Many ideas have been proposed to introduce some a-priori knowledge into the algorithm to speed up its learning rate. Recently also sparsity concepts
Markus Rupp, Stefan Schwarz
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Although the LMS algorithm is often preferred in practice due to its numerous positive implementation properties, once the parameter space to estimate becomes large, the algorithm suffers of slow learning. Many ideas have been proposed to introduce some a-priori knowledge into the algorithm to speed up its learning rate. Recently also sparsity concepts
Markus Rupp, Stefan Schwarz
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Comments on “Fractional LMS algorithm”
Signal Processing, 2017The purpose of this note is to point out that the recently proposed fractional least mean squares (FLMS) algorithm, whose derivation is based on fractional derivative, is not suitable for adaptive signal processing. Our claims are verified via extensive simulation results with comparison with the least mean squares (LMS) algorithm, indicating that the ...
Neil J. Bershad +2 more
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Fast and Robust Variable-Step-Size LMS Algorithm for Adaptive Beamforming
IEEE Antennas and Wireless Propagation Letters, 2020Conventional least-mean-square (LMS) algorithm is one of the most popular algorithms, which is widely used for adaptive beamforming. But the performance of the LMS algorithm degrades significantly because the constant step size is not suitable for ...
Babur Jalāl +2 more
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IEEE Transactions on Signal and Information Processing over Networks, 2023
The least mean square (LMS) algorithm of the graph signal processing (GSP) based on the mean square error criterion has a poor reconstruction effect when the graph sampling signal is disturbed by impulse noise.
Haiquan Zhao, Wang Xiang, Shaohui Lv
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The least mean square (LMS) algorithm of the graph signal processing (GSP) based on the mean square error criterion has a poor reconstruction effect when the graph sampling signal is disturbed by impulse noise.
Haiquan Zhao, Wang Xiang, Shaohui Lv
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Robust Bias-Compensated LMS Algorithm: Design, Performance Analysis and Applications
IEEE Transactions on Vehicular Technology, 2023This paper considers the problem of system parameter estimation using adaptive filter. Conventional adaptive algorithms will result in degraded performance in the presence of impulsive noise and biased estimation when the input signal is noisy.
Fuyi Huang +4 more
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An Optimized Zero-Attracting LMS Algorithm for the Identification of Sparse System
IEEE/ACM Transactions on Audio Speech and Language Processing, 2022This paper introduces an optimized zero-attractor to improve the performance of least mean square (LMS)-based algorithms for the identification of sparse system.
L. Luo, Wenjie Zhu
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A Robust Generalized Proportionate Diffusion LMS Algorithm for Distributed Estimation
IEEE Transactions on Circuits and Systems - II - Express Briefs, 2021This brief paper proposes a robust generalized proportionate diffusion Least Mean Square (LMS) algorithm for distributed estimation of a parameter vector in a network. The contribution of this brief is twofold.
Hadi Zayyani, A. Javaheri
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Stochastic Analysis of the Filtered-x LMS Algorithm for Active Noise Control
IEEE/ACM Transactions on Audio Speech and Language Processing, 2020The filtered-x least-mean-square (FxLMS) algorithm has been widely used for the active noise control. A fundamental analysis of the convergence behavior of the FxLMS algorithm, including the transient and steady-state performance, could provide some new ...
Feiran Yang, Jianfeng Guo, Jun Yang
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2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity.
Paulo S. R. Diniz +2 more
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In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity.
Paulo S. R. Diniz +2 more
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IEEE Communications Letters, 2000
Pilot symbol-assisted adaptive algorithms provide coherent detection for communication systems when the filtering coefficients, such as beamforming weights or equalizer coefficients, are converged. This property can be exploited to speed up the convergence of adaptive algorithms used.
Ying-Chang Liang, Francois P. S. Chin
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Pilot symbol-assisted adaptive algorithms provide coherent detection for communication systems when the filtering coefficients, such as beamforming weights or equalizer coefficients, are converged. This property can be exploited to speed up the convergence of adaptive algorithms used.
Ying-Chang Liang, Francois P. S. Chin
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