Jeng-Shin Sheu and Jenn-Kaie Lain and Tai-Kuo Woo and Jyh-Horng Wen
A Novel Convergence Accelerator for the LMS Adaptive Algorithm
922 - 926
2010
4
5
International Journal of Information and Communication Engineering
https://publications.waset.org/pdf/121
https://publications.waset.org/vol/41
World Academy of Science, Engineering and Technology
The least mean square (LMS) algorithmis one of the
most wellknown algorithms for mobile communication systems
due to its implementation simplicity. However, the main limitation
is its relatively slow convergence rate. In this paper, a booster
using the concept of Markov chains is proposed to speed up the
convergence rate of LMS algorithms. The nature of Markov
chains makes it possible to exploit the past information in the
updating process. Moreover, since the transition matrix has a
smaller variance than that of the weight itself by the central limit
theorem, the weight transition matrix converges faster than the
weight itself. Accordingly, the proposed Markovchain based
booster thus has the ability to track variations in signal
characteristics, and meanwhile, it can accelerate the rate of
convergence for LMS algorithms. Simulation results show that the
LMS algorithm can effectively increase the convergence rate and
meantime further approach the Wiener solution, if the
Markovchain based booster is applied. The mean square error is
also remarkably reduced, while the convergence rate is improved.
Open Science Index 41, 2010