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A Class of Diffusion Proportionate Subband Adaptive Filters for Sparse System Identification over Distributed Networks

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Abstract

This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalized subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variations, namely diffusion proportionate normalized subband adaptive filter (DPNSAF), diffusion \(\mu \)-law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms.

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Data Availability

The datasets (radio channel coefficients) analyzed during the current study are available in http://spib.linse.ufsc.br/microwave.html.

Notes

  1. The employed radio channel coefficients are available in http://spib.linse.ufsc.br/microwave.html.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

    Ahmad Pouradabi

  2. Department of Electrical Engineering, Malayer University, 65719-95863, Malayer, Iran

    Amir Rastegarnia & Sajad Zandi

  3. Electrical and Computer Engineering Department, American University in Dubai, Dubai, UAE

    Wael M. Bazzi

  4. School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Saeid Sanei

Authors
  1. Ahmad Pouradabi
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  2. Amir Rastegarnia
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  3. Sajad Zandi
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  4. Wael M. Bazzi
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  5. Saeid Sanei
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Correspondence to Amir Rastegarnia.

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Pouradabi, A., Rastegarnia, A., Zandi, S. et al. A Class of Diffusion Proportionate Subband Adaptive Filters for Sparse System Identification over Distributed Networks. Circuits Syst Signal Process 40, 6242–6264 (2021). https://doi.org/10.1007/s00034-021-01766-x

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  • DOI: https://doi.org/10.1007/s00034-021-01766-x

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