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Blind adaptive channel shortening with a generalized lag-hopping algorithm which employs squared auto-correlation minimization [GLHSAM].

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posted on 2009-12-04, 09:03 authored by Khaled Maatoug, Jonathon Chambers
A generalized blind lag-hopping adaptive channel shortening (GLHSAM) algorithm based upon squared auto-correlation minimization is proposed. This algorithm provides the ability to select a level of complexity at each iteration between the sum-squared autocorrelation minimization (SAM) algorithm due to Martin and Johnson and the single lag autocorrelation minimization (SLAM) algorithm proposed by Nawaz and Chambers whilst guaranteeing convergence to high signal to interference ratio (SIR). At each iteration a number of unique lags are chosen randomly from the available range so that on the average GLHSAM has the same cost as the SAM algorithm. The performance of the proposed GLHSAM algorithm is confirmed through simulation studies.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

MAATOUG, K. and CHAMBERS, J.A., 2008. Blind adaptive channel shortening with a generalized lag-hopping algorithm which employs squared auto-correlation minimization [GLHSAM]. IN: Proceedings of 2008 3rd International Conference on Systems and Networks Communications (ICSNC 2008), Sliema, Malta, 26-31 October, pp. 75-78.

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.

Language

  • en

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