A Novel Variable Step-size LMS Adaptive Filtering Algorithm and Its Comparison with Traditional Algorithms

This paper presents a new variable step-size LMS adaptive filtering algorithm that plays a significant role in signal processing applications. Compared to traditional algorithms, this approach demonstrates superior performance and enhanced adaptability through dynamic step-size adjustment based on error signal characteristics. The algorithm implementation typically involves calculating the step-size parameter μ(n) recursively using error-dependent functions, optimizing convergence speed while maintaining steady-state error performance. Through detailed comparative analysis with fixed step-size LMS and other adaptive filtering techniques, we demonstrate the algorithm's advantages and potential across various applications including system identification, noise cancellation, and channel equalization. The proposed method shows promising application prospects and research value in practical implementations due to its balance between convergence rate and misadjustment trade-offs.