Performance Analysis and Enhancement of Spline Adaptive Filtering based on Adaptive Step-size Variable Leaky Least Mean Square Algorithm

Abstract

This paper presents an adaptive step-size and variable leaky least mean square algorithm based on nonlinear adaptive filter with the adaptive lookup table using spline interpolation. An adaptive step-size approach is proposed with the energy of squared previous and present errors to boost up the convergence rate. A modified variable leaky mechanism is proposed with the optimal leaky parameter by using the recursion form. The proposed algorithm merges an adaptive step-size and a modified variable leaky method with least mean square algorithm for linear and nonlinear network part of spline adaptive filtering in term of fast convergence enhancement. Experimental results demonstrate that proposed algorithm can notably achieve a competitive performance on the convergence rate in comparison with the conventional least mean square algorithm for spline adaptive filter. Simulation results suggest that mean square error performance of proposed algorithm can be partially assessed using adaptive step-size with the variable leaky parameters indicating better than the conventional least mean square algorithm by 16.76%.