Title :
Hirschman optimal transform DFT block LMS algorithm
Author :
DeBrunner, Victor E. ; Alkhouli, Osama
Author_Institution :
Dept. of Elec. & Comp. Eng., Florida State Univ., Tallahassee, FL
fDate :
March 31 2008-April 4 2008
Abstract :
In this paper a new block LMS algorithm is introduced. This algorithm is based on a fast HOT convolution developed by our group. We call our algorithm the block HOT-DFT LMS algorithm. Our algorithm uses the premise that the filter size is much smaller than the block size. Our developed algorithm is very similar to the block DFT LMS algorithm, but provides a reduced computational complexity of about 30%. The computational efficiency of the block HOT-DFT LMS algorithm is verified and its convergence analysis is analyzed.
Keywords :
discrete Fourier transforms; least mean squares methods; DFT block; HOT convolution; Hirschman optimal transform; LMS algorithm; Adaptive filters; Algorithm design and analysis; Computational complexity; Computational efficiency; Convergence; Convolution; Filtering algorithms; Least squares approximation; Partitioning algorithms; Uncertainty; Adaptive filtering; Hirschman Uncertainty; Transform domain LMS algorithm;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2008.4518482
