Volume 13, Number 3 (September 2017)                   IJEEE 2017, 13(3): 213-218 | Back to browse issues page
Abstract:   (424 Views)

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.

Full-Text [PDF 1231 kb]   (240 Downloads)    
Type of Study: Research Paper | Subject: Signal Processing
Received: 2016/12/25 | Accepted: 2017/07/11 | Published: 2017/09/06