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Using Hierarchical Filters to Detect Sparseness in Unknown Channels

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4253))

Abstract

A novel algorithm particularly suited for determining and identifying the sparseness and the associated features within an unknown channel model is proposed.This is achieved by virtue of hierarchical filters which exhibit relatively steady state characteristics, when more than one of their constitutive sub-filters have to identify non-zero parts of the unknown channel. This way, sparseness is detected by comparing, in an on-line fashion, the performance of a hierarchical filter with that of the standard finite impulse response (FIR) filter trained by the least mean square (LMS) algorithm. In addition, the character and type of sparseness can be detected based on the architecture of the underlying hierarchical filter. The analysis is supported by simulations evaluated in a rigourous statistical framework.

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Author information

Authors and Affiliations

  1. Athens Information Technology, Greece

    C. Boukis & L. C. Polymenakos

Authors
  1. C. Boukis
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  2. L. C. Polymenakos
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Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2006 Springer-Verlag Berlin Heidelberg

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Boukis, C., Polymenakos, L.C. (2006). Using Hierarchical Filters to Detect Sparseness in Unknown Channels. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_155

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  • DOI: https://doi.org/10.1007/11893011_155

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46542-3

  • Online ISBN: 978-3-540-46544-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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