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 subfilter changes according to the largest decrease in mean square deviation. the simulation results show that the proposed vss-wtdlms has faster convergence rate and lower misadjustment than ordinary wtdlms.