NDLI: A Novel Convergence Accelerator for the LMS Adaptive Filter

Content Provider	Springer Nature Link
Author	Sheu, Jeng Shin Woo, Tai Kuo Wen, Jyh Horng
Copyright Year	2011
Abstract	Due to its ease of implementation, the least mean square (LMS) algorithm is one of the most well-known algorithms for mobile communication systems. However, the main limitation of this approach is its relatively slow convergence rate. This paper proposes a booster using the Markov chain concept to speed up the convergence rate of LMS algorithms. The nature of Markov chains makes it possible to exploit past information in the updating process. According to the central limit theorem, the transition matrix has a smaller variance than that of the weight itself. As a result, the weight transition matrix converges faster than the weight itself. Therefore, the proposed Markov-chain based booster is able to track variations in signal characteristics and simultaneously accelerate the rate of convergence for LMS algorithms. Simulation results show that the Markov-chain based booster allows an LMS algorithm to effectively increase the convergence rate and further approach the Wiener solution. This approach also markedly reduces the mean square error while improving the convergence rate.
Starting Page	283
Ending Page	300
Page Count	18
File Format	PDF
ISSN	0278081X
Journal	Circuits, Systems, and Signal Processing
Volume Number	31
Issue Number	1
e-ISSN	15315878
Language	English
Publisher	SP Birkhäuser Verlag Boston
Publisher Date	2011-03-26
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	LMS Convergence rate Transition matrix MSSDE Electronics and Microelectronics, Instrumentation Circuits and Systems Signal, Image and Speech Processing Electrical Engineering
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing

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