Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers (1994)
Pacific Grove, CA, USA
Oct. 31, 1994 to Nov. 2, 1994
Xiaohui Li , Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA
W.K. Jenkins , Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA
The block-based LMS algorithm (BLMS) is an efficient adaptive filtering algorithm aimed at increasing the convergence speed and reducing the computational complexity. The basic principle of the BLMS algorithm is that the filter coefficients remain unchanged during the processing of each data block, and are updated only once per block. The convergence properties of the unconstrained frequency-domain block LMS adaptive algorithm are analyzed. The learning characteristics of the unconstrained case are compared with the constrained case via computer simulation. It is shown that the unconstrained algorithm has a slower convergence rate and smaller stable range of step size than that of the constrained algorithm.<
frequency-domain analysis, adaptive signal processing, adaptive filters, filtering theory, least mean squares methods, convergence of numerical methods, computational complexity
Xiaohui Li and W. Jenkins, "Convergence properties of the frequency-domain block-LMS adaptive algorithm," Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers(ACSSC), Pacific Grove, CA, USA, 1995, pp. 1515-1519.