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Tensor Factorisation Approach for Separation of Convolutive Complex Communication Signals

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Latent Variable Analysis and Signal Separation (LVA/ICA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9237))

Abstract

Communication signals such as multiple constellation signals have complex waveforms and may have multipath source reflection and noncircularity problems. Tensor based source separation techniques have become increasingly popular for various applications as they exploit different inherent diversities of the sources. In this paper, a tensor based convolutive source separation algorithm is developed based on PARAFAC2. The optimisation technique is based on the direct model fitting of PARAFAC and augmented statistics. The proposed method is evaluated using simulated data with multiple pathways and various noncircularity levels. Simulation results confirm the superiority of the proposed method over the existing popular techniques.

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Authors and Affiliations

  1. Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK

    Samaneh Kouchaki & Saeid Sanei

Authors
  1. Samaneh Kouchaki
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  2. Saeid Sanei
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Correspondence to Samaneh Kouchaki .

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Editors and Affiliations

  1. Inria, Villers-les-Nancy, France

    Emmanuel Vincent

  2. Tel Aviv University, Tel-Aviv, Israel

    Arie Yeredor

  3. Technical University of Libere, Liberec, Czech Republic

    Zbyněk Koldovský

  4. The Czech Academy of Sciences, Prague, Czech Republic

    Petr Tichavský

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Kouchaki, S., Sanei, S. (2015). Tensor Factorisation Approach for Separation of Convolutive Complex Communication Signals. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-22482-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22481-7

  • Online ISBN: 978-3-319-22482-4

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