{"title":"A Novel Convergence Accelerator for the LMS Adaptive Algorithm","authors":"Jeng-Shin Sheu, Jenn-Kaie Lain, Tai-Kuo Woo, Jyh-Horng Wen","country":null,"institution":"","volume":41,"journal":"International Journal of Information and Communication Engineering","pagesStart":922,"pagesEnd":927,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/121","abstract":"The least mean square (LMS) algorithmis one of the\r\nmost well-known algorithms for mobile communication systems\r\ndue to its implementation simplicity. However, the main limitation\r\nis its relatively slow convergence rate. In this paper, a booster\r\nusing the concept of Markov chains is proposed to speed up the\r\nconvergence rate of LMS algorithms. The nature of Markov\r\nchains makes it possible to exploit the past information in the\r\nupdating process. Moreover, since the transition matrix has a\r\nsmaller variance than that of the weight itself by the central limit\r\ntheorem, the weight transition matrix converges faster than the\r\nweight itself. Accordingly, the proposed Markov-chain based\r\nbooster thus has the ability to track variations in signal\r\ncharacteristics, and meanwhile, it can accelerate the rate of\r\nconvergence for LMS algorithms. Simulation results show that the\r\nLMS algorithm can effectively increase the convergence rate and\r\nmeantime further approach the Wiener solution, if the\r\nMarkov-chain based booster is applied. The mean square error is\r\nalso remarkably reduced, while the convergence rate is improved.","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 41, 2010"}