Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks
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, an...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/14/12/373 |
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| author | Okiemute Roberts Omasheye Samuel Azi Joseph Isabona Agbotiname Lucky Imoize Chun-Ta Li Cheng-Chi Lee |
| author_facet | Okiemute Roberts Omasheye Samuel Azi Joseph Isabona Agbotiname Lucky Imoize Chun-Ta Li Cheng-Chi Lee |
| author_sort | Okiemute Roberts Omasheye |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:34:44Z |
| format | Article |
| id | doaj.art-e28a50718f5543feaf4c5f430cd44f80 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T16:34:44Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-e28a50718f5543feaf4c5f430cd44f802023-11-24T14:58:44ZengMDPI AGFuture Internet1999-59032022-12-01141237310.3390/fi14120373Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular NetworksOkiemute Roberts Omasheye0Samuel Azi1Joseph Isabona2Agbotiname Lucky Imoize3Chun-Ta Li4Cheng-Chi Lee5Department of Physics, Delta State College of Education, Mosogar 331101, NigeriaDepartment of Physics, University of Benin, Benin City 300103, NigeriaDepartment of Physics, Federal University Lokoja, Lokoja 260101, NigeriaDepartment of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, NigeriaProgram of Artificial Intelligence and Information Security, Fu Jen Catholic University, New Taipei City 24206, TaiwanResearch and Development Center for Physical Education, Health, and Information Technology, Department of Library and Information Science, Fu Jen Catholic University, New Taipei City 24206, TaiwanThe 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.https://www.mdpi.com/1999-5903/14/12/373path loss measurementsignal strength intensityparticle swarm optimizationrandom foresthybrid RF-PS modelwireless network modeling |
| spellingShingle | Okiemute Roberts Omasheye Samuel Azi Joseph Isabona Agbotiname Lucky Imoize Chun-Ta Li Cheng-Chi Lee Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks Future Internet path loss measurement signal strength intensity particle swarm optimization random forest hybrid RF-PS model wireless network modeling |
| title | Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks |
| title_full | Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks |
| title_fullStr | Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks |
| title_full_unstemmed | Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks |
| title_short | Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks |
| title_sort | joint random forest and particle swarm optimization for predictive pathloss modeling of wireless signals from cellular networks |
| topic | path loss measurement signal strength intensity particle swarm optimization random forest hybrid RF-PS model wireless network modeling |
| url | https://www.mdpi.com/1999-5903/14/12/373 |
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