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Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks

Author

Listed:
  • Okiemute Roberts Omasheye

    (Department of Physics, Delta State College of Education, Mosogar 331101, Nigeria)

  • Samuel Azi

    (Department of Physics, University of Benin, Benin City 300103, Nigeria)

  • Joseph Isabona

    (Department of Physics, Federal University Lokoja, Lokoja 260101, Nigeria)

  • Agbotiname Lucky Imoize

    (Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
    Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany)

  • Chun-Ta Li

    (Program of Artificial Intelligence and Information Security, Fu Jen Catholic University, New Taipei City 24206, Taiwan
    Department of Information Management, Tainan University of Technology, Tainan City 71002, Taiwan)

  • Cheng-Chi Lee

    (Research and Development Center for Physical Education, Health, and Information Technology, Department of Library and Information Science, Fu Jen Catholic University, New Taipei City 24206, Taiwan
    Department of Computer Science and Information Engineering, Asia University, Taichung City 41354, Taiwan)

Abstract

The accurate and reliable predictive estimation of signal attenuation loss is of prime importance in radio resource management. During wireless network design and planning, a reliable path loss model is required for optimal predictive estimation of the received signal strength, coverage, quality, and signal interference-to-noise ratio. A set of trees (100) on the target measured data was employed to determine the most informative and important subset of features, which were in turn employed as input data to the Particle Swarm (PS) model for predictive path loss analysis. The proposed Random Forest (RF-PS) based model exhibited optimal precision performance in the real-time prognostic analysis of measured path loss over operational 4G LTE networks in Nigeria. The relative performance of the proposed RF-PS model was compared to the standard PS and hybrid radial basis function-particle swarm optimization (RBF-PS) algorithm for benchmarking. Generally, results indicate that the proposed RF-PS model gave better prediction accuracy than the standard PS and RBF-PS models across the investigated environments. The projected hybrid model would find useful applications in path loss modeling in related wireless propagation environments.

Suggested Citation

  • Okiemute Roberts Omasheye & Samuel Azi & Joseph Isabona & Agbotiname Lucky Imoize & Chun-Ta Li & Cheng-Chi Lee, 2022. "Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks," Future Internet, MDPI, vol. 14(12), pages 1-26, December.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:12:p:373-:d:1001042
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    References listed on IDEAS

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    1. M. Piacentini & F. Rinaldi, 2011. "Path loss prediction in urban environment using learning machines and dimensionality reduction techniques," Computational Management Science, Springer, vol. 8(4), pages 371-385, November.
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    Cited by:

    1. Ganjar Alfian & Muhammad Syafrudin & Norma Latif Fitriyani & Sahirul Alam & Dinar Nugroho Pratomo & Lukman Subekti & Muhammad Qois Huzyan Octava & Ninis Dyah Yulianingsih & Fransiskus Tatas Dwi Atmaji, 2023. "Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags," Future Internet, MDPI, vol. 15(3), pages 1-16, March.

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