Abstract
This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square (VEDNSS LMS) algorithm. Adopting VEDNSS LMS provides fast convergence at early stages of adaptation while ensuring small final misadjustment. An analysis of convergence and steady-state performance for zero-mean Gaussian inputs is provided. Simulation results comparing the proposed algorithm to existing algorithms indicate its superior performance under various noise and frequency environments.
