Variable Step-Size $\ell _{0}$-Norm Constraint NLMS Algorithms Based on Novel Mean Square Deviation Analyses

Lee, Minho; Park, Taesu; Park, PooGyeon

doi:10.1109/tsp.2022.3231187

Cited by 8 publications

(4 citation statements)

References 76 publications

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“…To verify the robustness of the algorithm when the channel changes abruptly, the channel changes at 4 × 10 4 , and the change effect is achieved by inverting the channel in this experiment. The normalized mean square deviation (NMSD) curve is used to measure the performance of the algorithm in this paper [25], which reflects the error between the filter estimate and the actual channel and can intuitively show the convergence effect of the algorithm. The expression is as shown in Equation (11).…”

Section: Simulation Analysis Of the Proposed Algorithmmentioning

confidence: 99%

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Zhu,

Wang,

Cai

et al. 2024

Electronics

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Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm.

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Section: Simulation Analysis Of the Proposed Algorithmmentioning

confidence: 99%

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Zhu,

Wang,

Cai

et al. 2024

Electronics

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“…where M ≥ M is the virtual filter length that considers colored input signals [11,12]. When the input signal is a white Gaussian input, the matrix Λ Tr(R) is approximately equal to 1 M I M .…”

Section: Msd Analysis Of the Nlms Algorithm Using The Random Walk Modelmentioning

confidence: 99%

“…Several algorithms have been proposed to address this challenge, such as the variable step size (VSS) and variable regularization (VR) algorithms. These algorithms aim to enhance the convergence behaviors and steady-state misalignment [7][8][9][10][11][12][13][14]. Because many of these algorithms were developed under the assumption of a time-invariant system to simplify the algorithm design, they cannot perform optimally when the system undergoes significant changes.…”

Section: Introductionmentioning

confidence: 99%

A Novel NLMS Algorithm for System Identification

et al. 2023

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In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size and regularization parameters, for the rapid convergence of the colored input signals. We verified that the proposed algorithm exhibited faster convergence than existing algorithms, even in scenarios of sudden system changes.

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“…In the work presented in [23], variable step-size l 0 -norm constraint NLMS algorithms are developed for sparse system identification. The variable step-size schemes are derived by minimizing the forthcoming mean square deviations (MSDs), and the optimal step-size in each iteration is determined through an upper bound of the MSD derivation.…”

Section: Introductionmentioning

confidence: 99%

A Variable Step-Size Regularization-Based Quasi-Newton Adaptive Filter for System Identification

Bekrani,

Zayyani

2024

IEEE Access

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The family of least mean square (LMS) based adaptive filtering algorithms suffers from convergence performance limitation due to the sensitivity of such algorithms to the eigenvalue spread of the input correlation matrix. The quasi-Newton family of adaptive filtering algorithms addresses this limitation, but its performance is restricted by the estimation accuracy of the correlation matrix inverse, especially for highly correlated input signals. Furthermore, the convergence rate and the steady-state performance of both LMS and quasi-Newton families are thoroughly depending on their step-sizes. In this paper, a variable stepsize regularized quasi-Newton adaptive algorithm is proposed in the context of system identification. In this algorithm, inspired by the matrix inversion lemma, a modified regularized matrix inverse with a time-varying regularization is computed such that during the convergence, the contribution of matrix inverse in the weight update is reduced, resulting in a more noise-robust algorithm. The paper further provides a convergence analysis of the proposed quasi-Newton algorithm, wherein a variable step-size is proposed to achieve a high initial convergence rate and a low steady-state error in the context of system identification applications.

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Variable Step-Size $\ell _{0}$-Norm Constraint NLMS Algorithms Based on Novel Mean Square Deviation Analyses

Cited by 8 publications

References 76 publications

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

A Novel NLMS Algorithm for System Identification

A Variable Step-Size Regularization-Based Quasi-Newton Adaptive Filter for System Identification

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