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Title: A novel single lag auto-correlation minimization (SLAM) algorithm for blind adaptive channel shortening
Authors: Nawaz, Rab
Chambers, Jonathon
Issue Date: 2005
Publisher: © IEEE
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.
Abstract: A 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.
Description: 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.
Version: Published
DOI: 10.1109/ICASSP.2005.1415852
URI: https://dspace.lboro.ac.uk/2134/5666
ISBN: 0780388747
Appears in Collections:Conference Papers and Contributions (Mechanical, Electrical and Manufacturing Engineering)

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