High Speed LMS Adaptive Filtering

Article type: Research Article

Authors: Kazlauskas, Kazys

Affiliations: Institute of Mathematics and Informatics, Akademijos 4, 2600 Vilnius, Lithuania. E-mail: kazlausk@ktl.mii.lt

Abstract: In this paper we show that the least mean square (LMS) algorithm can be speeded up without changing any of its adaptive characteristics. The parallel LMS adaptive filtering algorithm and its modifications are presented. High speed is achieved by increasing the parallelism in the LMS adaptive algorithm through a proper modification of the LMS adaptive algorithm. An iterative procedures for efficient computation of the lower triangular inverse matrix and the input signal covariance matrix are presented.

Keywords: parallel, least mean square, adaptive filtering

DOI: 10.3233/INF-1998-9203

Journal: Informatica, vol. 9, no. 2, pp. 161-171, 1998