Abstract
A general framework is proposed to derive proportionate adaptive algorithms for sparse system identification. The proposed algorithmic framework employs the convex optimization and covers many traditional proportionate algorithms. Meanwhile, based on this framework, some novel proportionate algorithms could be derived too. In the simulations, we compare the new derived proportionate algorithm with the traditional ones and demonstrate that it could provide faster convergence rate and tracking performance for both white and colored input in sparse system identification.
Recommended Citation
J. Liu and S. L. Grant, "A Generalized Proportionate Adaptive Algorithm based on Convex Optimization," 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings, pp. 748 - 752, article no. 6889344, Institute of Electrical and Electronics Engineers, Sep 2014.
The definitive version is available at https://doi.org/10.1109/ChinaSIP.2014.6889344
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
convex optimization; echo cancellation; proportionate adaptive algorithm
International Standard Book Number (ISBN)
978-147995403-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
03 Sep 2014
