Review and Comparison of Variable Step-Size LMS Algorithms
نویسندگان
چکیده
The inherent feature of the Least Mean Squares (LMS) algorithm is the step size, and it requires careful adjustment. Small step size, required for small excess mean square error, results in slow convergence. Large step size, needed for fast adaptation, may result in loss of stability. Therefore, many modifications of the LMS algorithm, where the step size changes during the adaptation process depending on some particular characteristics, were and are still being developed. The paper reviews seventeen of the best known variable step-size LMS (VS-LMS) algorithms to the degree of detail that allows to implement them. The performance of the algorithms is compared in three typical applications: parametric identification, line enhancement, and adaptive noise cancellation. The paper suggests also one general modification that can simplify the choice of the upper bound for the step size, which is a crucial parameter for many VS-LMS algorithms.
منابع مشابه
The Wavelet Transform-Domain LMS Adaptive Filter Algorithm with Variable Step-Size
The wavelet transform-domain least-mean square (WTDLMS) algorithm uses the self-orthogonalizing technique to improve the convergence performance of LMS. In WTDLMS algorithm, the trade-off between the steady-state error and the convergence rate is obtained by the fixed step-size. In this paper, the WTDLMS adaptive algorithm with variable step-size (VSS) is established. The step-size in each subf...
متن کاملA Family of Variable Step-Size Normalized Subband Adaptive Filter Algorithms Using Statistics of System Impulse Response
This paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. The proposed algorithm uses the prior knowledge of the system impulse response statistics and the optimal step-size vector is obtained by minimizing the mean-square deviation(MSD). In comparison with NSAF, the VSS-NSAF algorithm has faster convergence speed and lower MSD. To reduce the computa...
متن کاملA Unified Analysis Approach for LMS-based Variable Step-Size Algorithms
The least-mean-squares (LMS) algorithm is the most popular algorithm in adaptive filtering. Several variable step-size strategies have been suggested to improve the performance of the LMS algorithm. These strategies enhance the performance of the algorithm but a major drawback is the complexity in the theoretical analysis of the resultant algorithms. Researchers use several assumptions to find ...
متن کاملNon Stationary Noise Removal from Speech Signals using Variable Step Size Strategy
The aim of this paper is to implement various adaptive noise cancellers (ANC) for speech enhancement based on gradient descent approach, namely the least-mean square (LMS) algorithm and then enhanced to variable step size strategy. In practical application of the LMS algorithm, a key parameter is the step size. As is well known, if the step size is large, the convergence rate of the LMS algorit...
متن کاملIterative-Promoting Variable Step-size Least Mean Square Algorithm For Adaptive Sparse Channel Estimation
Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity. In the scenarios of sparse channel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS) and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channel sparsity. However, these proposed algorithms may hard to make tradeoff between convergence speed ...
متن کامل