Random partial update sum-squared autocorrelation minimization algorithm.pdf (371.11 kB)
Random partial update sum-squared autocorrelation minimization algorithm for channel shortening (RPUSAM).
conference contribution
posted on 2009-12-04, 09:15 authored by M. Grira, Jonathon ChambersPartial updating is an effective method for
reducing computational complexity in adaptive filter implementations.
In this work, a novel random partial update
sum-squared auto-correlation minimization (RPUSAM)
algorithm is proposed. This algorithm has low computational
complexity whilst achieving improved convergence
performance, in terms of achievable bit rate, over a
partial update sum-squared auto-correlation minimization
(PUSAM) algorithm with a deterministic coefficient update
strategy. The performance advantage of the RPUSAM
algorithm is shown on eight different carrier serving area
test loops (CSA) channels and comparisons are made with
the original SAM and the PUSAM algorithms.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Citation
GRIRA, M. and CHAMBERS, J.A., 2008. Random partial update sum-squared autocorrelation minimization algorithm for channel shortening (RPUSAM). IN: Proceedings of 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP 2008), St. Julians, Malta, 12-14 March, pp. 1400-1403.Publisher
© IEEEVersion
- VoR (Version of Record)
Publication date
2008Notes
This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Language
- en
