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A WiSARD Network Approach for 5G MIMO Beam Selection

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1174))

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Abstract

The integration of context information and machine learning techniques can enhance the capabilities of 5G/6G networks when dealing with the beam selection problem. This paper proposes the use of a Weightless Neural Network (WiSARD) with multimodal data as input to address this problem. The performance of the WiSARD is compared to classic machine learning algorithms (KNN, Decision Tree, SVC, Random Forest) based on the top-k accuracy in a vehicular network. The simulation results indicate that the WiSARD is a competitive method for this scenario and can be a valuable asset for future cellular networks.

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Notes

  1. 1.

    https://www.lasse.ufpa.br/raymobtime/.

  2. 2.

    https://cadmapper.com.

  3. 3.

    https://www.openstreetmap.org/#map=4/-15.13/-53.19.

  4. 4.

    https://sumo.dlr.de/userdoc/.

  5. 5.

    https://www.remcom.com/wireless-insite-em-propagation-software/.

  6. 6.

    https://github.com/IAZero/wisardpkg.

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Acknowledgments

This work was partially supported by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), RNP with resources from MCTIC (under the grant 01250.075413/2018-04 from the Radiocommunication Reference Center project of the National Institute of Telecommunications, Brazil) and the MCTI/CGI.br and the São Paulo Research Foundation (FAPESP) (under grants 2018/23097-3 (SFI2), 2020/05127-2 (SAMURAI) and 2020/05152-7 (PROFISSA)). Douglas O. Cardoso acknowledges the financial support by the portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia, FCT) through grants with the following DOIs: 10.54499/UIDB/00022/2020, 10.54499/UIDP/00022/2020, 10.54499/UIDB/05567/2020, and 10.54499/UIDP/05567/2020.

Author information

Authors and Affiliations

  1. PESC, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Joanna C. Manjarres & José Ferreira de Rezende

  2. Centre of Linguistics, Faculty of Arts and Humanities, University of Porto, Porto, Portugal

    Douglas O. Cardoso

  3. Smart Cities Research Center, Polytechnic Institute of Tomar, Tomar, Portugal

    Douglas O. Cardoso

  4. LASSE, Federal University of Para, Belém, Brazil

    Aldebaro Klautau

Authors
  1. Joanna C. Manjarres
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  2. Douglas O. Cardoso
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  3. Aldebaro Klautau
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  4. José Ferreira de Rezende
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Corresponding author

Correspondence to Joanna C. Manjarres .

Editor information

Editors and Affiliations

  1. LIP6, BC169, Sorbonne Université, Paris, France

    Marie-Jeanne Lesot

  2. Departamento de Engenharia Mecânica, University of Lisbon, Lisbon, Portugal

    Susana Vieira

  3. Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Marek Z. Reformat

  4. INESC-ID,Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    João Paulo Carvalho

  5. INESC-ID, Av. das Forças Armadas, 1649-026, ISCTE - University Institute of Lisbon, Lisboa, Portugal

    Fernando Batista

  6. LIP6, BC169, Place Jussieu 4, 75005, Sorbonne University, Paris, France

    Bernadette Bouchon-Meunier

  7. Machine Intelligence Institute, 715 North Avenue, Iona Collage, NEW ROCHELLE, NY, USA

    Ronald R. Yager

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Manjarres, J.C., Cardoso, D.O., Klautau, A., de Rezende, J.F. (2024). A WiSARD Network Approach for 5G MIMO Beam Selection. In: Lesot, MJ., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2024. Lecture Notes in Networks and Systems, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-031-74003-9_29

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