Analysis of the convergence condition of LMS adaptive digital filter using distributed arithmetic

作     者：Takahashi, K Tsunekawa, Y Tayama, N Seki, K 

作者机构：Iwate Ind Technol Jr Coll Iwate 0283615 Japan Iwate Univ Fac Engn Morioka Iwate 0208551 Japan 

出 版 物：《IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES》 (IEICE Trans Fund Electron Commun Comput Sci)

年 卷 期：2002年第E85A卷第6期

页      面：1249-1256页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：distributed arithmetic LMS algorithm adaptive function space convergence condition offset bias 

摘      要：An LMS adaptive digital filter using distributed arithmetic (DA-ADF) has been proposed. Cowan and others proposed the DA adaptive algorithm with offset binary coding for the simple derivation of an algorithm and the use of an odd-symmetry property of adaptive function space (AFS) [3], [5], [10]. However, we indicated that a convergence speed of this DA adaptive algorithm degraded extremely by our computer simulations [6]. To overcome these problems, we have proposed the DA adaptive algorithm generalized with two s complement representation and effective architectures. Our DA-ADF has performances of a high speed, small output latency, a good convergence speed, small-scale hardware and lower power dissipation for higher order, simultaneously. In this paper, we analyze a convergence condition of DA adaptive algorithm that has never been considered theoretically [8], [9]. From this analysis, we indicate that the convergence speed is depended on a distribution of eigenvalues of an auto-correlation matrix of an extended input signal vector [8], [9]. Furthermore, we obtain the eigenvalues theoretically. As a result, we clearly show that our DA-ADF has an advantage of the conventional DA-ADF in the convergence speed.