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A blind lag-hopping adaptive channel shortening algorithm based upon squared auto-correlation minimization (LHSAM)

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conference contribution
posted on 30.11.2009, 13:45 by M. Grira, Jonathon Chambers
Recent analytical results due to Walsh, Martin and Johnson showed that optimizing the single lag autocorrelation minimization (SLAM) cost does not guarantee convergence to high signal to interference ratio (SIR), an important metric in channel shortening applications. We submit that we can overcome this potential limitation of the SLAM algorithm and retain its computational complexity advantage by minimizing the square of single autocorrelation value with randomly selected lag. Our proposed lag-hopping adaptive channel shortening algorithm based upon squared autocorrelation minimization (LHSAM) has, therefore, low complexity as in the SLAM algorithm and, more importantly, a low average LHSAM cost can guarantee to give a high SIR as for the SAM algorithm. Simulation studies are included to confirm the performance of the LHSAM algorithm.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

GRIRA, M and CHAMBERS, J., 2008. A blind lag-hopping adaptive channel shortening algorithm based upon squared auto-correlation minimization (LHSAM). IN: Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing 2008. ICASSP 2008, Las Vegas, Nevada, 31 March-4 April 2008, pp. 3569 - 3572

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2008

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

9781424414833

ISSN

1520-6149

Language

en