TY - JFULL AU - Jeng-Shin Sheu and Jenn-Kaie Lain and Tai-Kuo Woo and Jyh-Horng Wen PY - 2010/6/ TI - A Novel Convergence Accelerator for the LMS Adaptive Algorithm T2 - International Journal of Information and Communication Engineering SP - 921 EP - 926 VL - 4 SN - 1307-6892 UR - https://publications.waset.org/pdf/121 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 41, 2010 N2 - The least mean square (LMS) algorithmis one of the most well-known 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 Markov-chain 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 Markov-chain based booster is applied. The mean square error is also remarkably reduced, while the convergence rate is improved. ER -