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A novel single lag auto-correlation minimization (SLAM) algorithm for blind adaptive channel shortening

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conference contribution
posted on 2009-12-14, 12:20 authored by Rab Nawaz, Jonathon Chambers
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.

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

© IEEE

Version

  • VoR (Version of Record)

Publication date

2005

Notes

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

0780388747

Language

  • en

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