A novel single lag auto-correlation minimization (SLAM) algorithm.pdf (168.85 kB)
A novel single lag auto-correlation minimization (SLAM) algorithm for blind adaptive channel shortening
conference contribution
posted on 2009-12-14, 12:20 authored by Rab Nawaz, Jonathon ChambersA blind adaptive channel shortening algorithm based on minimizing
the sum of the squared autocorrelations (SAM) of
the effective channel was recently proposed. We submit
that identical channel shortening can be achieved by minimizing
the square of only a single autocorrelation. Our
proposed single lag autocorrelation minimization (SLAM)
algorithm has, therefore, very low complexity and also it
does not require, a priori, the knowledge of the length of the
channel. We also constrain the autocorrelation minimization
with a novel stopping criterion so that the shortening
signal to noise ratio (SSNR) of the effective channel is not
minimized by the autocorrelation minimization. The simulations
have shown that SLAM achieves higher bit rates
than SAM.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Citation
NAWAZ, R., and CHAMBERS, J.A., 2005. A novel single lag auto-correlation minimization (SLAM) algorithm for blind adaptive channel shortening. IN: Proceedings of 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, Pennsylvania, USA, 18-23 March, Vol.3, pp. 885-888.Publisher
© IEEEVersion
- VoR (Version of Record)
Publication date
2005Notes
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.ISBN
0780388747Language
- en