Low Cost Parallel Adaptive Filter Structures

Author

Cheng, Chao ; Parhi, Keshab K.

Author_Institution

Dept. of Electr. & Comput. Eng., Minnesota Univ., Minneapolis, MN

fYear

2005

fDate

Oct. 28 2005-Nov. 1 2005

Firstpage

354

Lastpage

358

Abstract

In this paper, we present two parallel LMS adaptive filtering algorithms with low hardware. The proposed parallel algorithm 1 doesn´t alter the input-output behavior and saves large amount of hardware cost of previous designs, especially when the parallelism level is high. For example, it saves 68.4% of the multiplications and 4.7% of the additions, of those of prior fast parallel adaptive filtering algorithms when parallelism level is 72 and the filter length N is large. The proposed parallel algorithm 2, while maintaining the same performance, can further save 5.56% to 12.5% of the multipliers and 8.54% to 24.9% of the additions when the level of parallelism varies from 3 to 72

Keywords

adaptive filters; filtering theory; least mean squares methods; matrix algebra; input-output behavior; least mean squared algorithm; parallel adaptive filtering algorithms; Adaptive filters; Convolution; Costs; Filtering algorithms; Finite impulse response filter; Hardware; Least squares approximation; Parallel algorithms; Parallel processing; Pipeline processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2005. Conference Record of the Thirty-Ninth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

1-4244-0131-3

Type

conf

DOI

10.1109/ACSSC.2005.1599767

Filename

1599767