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http://dx.doi.org/10.7776/ASK.2009.28.8.815

Categorized VSSLMS Algorithm  

Kim, Seon-Ho (서울대학교 전기컴퓨터공학부)
Chon, Sang-Bae (서울대학교 전기컴퓨터공학부)
Lim, Jun-Seok (세종대학교 전자공학과)
Sung, Koeng-Mo (서울대학교 전기컴퓨터공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.28, no.8, 2009 , pp. 815-821 More about this Journal
Abstract
Information processing in variable and noisy environments is usually accomplished by means of adaptive filters. Among various adaptive algorithms, Least Mean Square (LMS) has become the most popular for its robustness, good tracking capabilities and simplicity, both in terms of computational load and easiness of implementation. In practical application of the LMS algorithm, the most important key parameter is the Step Size. As is well known, if the Step Size is large, the convergence rate of the algorithm will be rapid, but the steady state mean square error (MSE) will increase. On the other hand, if the Step Size is small, the steady state MSE will be small, but the convergence rate will be slow. Many researches have been proposed to alleviate this drawback by using a variable Step Size. In this paper, a new variable Step Size LMS(VSSLMS) called Categorized VSSLMS (CVSSLMS) is proposed. CVSSLMS updates the Step Size by categorizing the current status of the gradient, hence significantly improves the convergence rate. The performance of the proposed algorithm was verified from the view point of convergence rate, Excessive Mean Square Error(EMSE), and complexity through experiments.
Keywords
Adaptive Algorithm; Variable Step Size; Interference Signal Reduction; LMS; Echo Cancellation;
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