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

Okiemute Roberts Omasheye, Samuel Azi, Joseph Isabona, Agbotiname Lucky Imoize, Chun-Ta Li () and Cheng-Chi Lee
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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
Chun-Ta Li: Program of Artificial Intelligence and Information Security, Fu Jen Catholic University, New Taipei City 24206, 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

Future Internet, 2022, vol. 14, issue 12, 1-26

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.

Keywords: path loss measurement; signal strength intensity; particle swarm optimization; random forest; hybrid RF-PS model; wireless network modeling (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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