Hybrid Levenberg–Marquardt and LSBoosting Ensemble Algorithms for Optimal Signal Attenuation Modeling and Coverage Analysis
ABSTRACT This paper proposes and engages the Levenberg–Marquardt algorithm method via regression to optimally model and predict real‐time signal strength values acquired via telecom software investigation tools in LTE cellular networks. To further improve the Levenberg–Marquardt method, which is som...
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| Vydané v: | International journal of communication systems Ročník 38; číslo 8 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Chichester
Wiley Subscription Services, Inc
25.05.2025
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| ISSN: | 1074-5351, 1099-1131 |
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| Abstract | ABSTRACT
This paper proposes and engages the Levenberg–Marquardt algorithm method via regression to optimally model and predict real‐time signal strength values acquired via telecom software investigation tools in LTE cellular networks. To further improve the Levenberg–Marquardt method, which is sometimes prone to parameter evaporation on high dimensional data with high bias or variance issues during the application, we explore the Least Square Boosting (LSBoosting) ensemble algorithms. The combined signal predictive modeling procedure is termed the hybrid LSBoost‐LM method. First, when the proposed hybrid LSBoost‐LM method was engaged for real‐time extrapolative signal analysis, the results displayed excellent root mean square error precision accuracies compared with two other standards, Bag‐LM and LM methods. As a case in point, the LSBoost‐LM method achieved 2.15, 3.44, 3.33, 1.31, and 2.19 dB RMSE values at different prediction study locations, which are relatively lower compared with the Bag‐LM and standard LM methods that achieved higher RMSE values of 3.38, 3.86, 4.07, 2.28, 3.98 dB and 5.57, 5.52, 5.14, 3.67, 4.56 dB, respectively. Secondly, applying the hybridized model produced up to 93.39% cell area coverage quality and 89.18% fringe cell area coverage quality across the eNodeB study locations. The proposed method can assist practicing RF network planners in realistic cell coverage quality estimation and analysis of related wireless networks. |
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| AbstractList | This paper proposes and engages the Levenberg–Marquardt algorithm method via regression to optimally model and predict real‐time signal strength values acquired via telecom software investigation tools in LTE cellular networks. To further improve the Levenberg–Marquardt method, which is sometimes prone to parameter evaporation on high dimensional data with high bias or variance issues during the application, we explore the Least Square Boosting (LSBoosting) ensemble algorithms. The combined signal predictive modeling procedure is termed the hybrid LSBoost‐LM method. First, when the proposed hybrid LSBoost‐LM method was engaged for real‐time extrapolative signal analysis, the results displayed excellent root mean square error precision accuracies compared with two other standards, Bag‐LM and LM methods. As a case in point, the LSBoost‐LM method achieved 2.15, 3.44, 3.33, 1.31, and 2.19 dB RMSE values at different prediction study locations, which are relatively lower compared with the Bag‐LM and standard LM methods that achieved higher RMSE values of 3.38, 3.86, 4.07, 2.28, 3.98 dB and 5.57, 5.52, 5.14, 3.67, 4.56 dB, respectively. Secondly, applying the hybridized model produced up to 93.39% cell area coverage quality and 89.18% fringe cell area coverage quality across the eNodeB study locations. The proposed method can assist practicing RF network planners in realistic cell coverage quality estimation and analysis of related wireless networks. ABSTRACT This paper proposes and engages the Levenberg–Marquardt algorithm method via regression to optimally model and predict real‐time signal strength values acquired via telecom software investigation tools in LTE cellular networks. To further improve the Levenberg–Marquardt method, which is sometimes prone to parameter evaporation on high dimensional data with high bias or variance issues during the application, we explore the Least Square Boosting (LSBoosting) ensemble algorithms. The combined signal predictive modeling procedure is termed the hybrid LSBoost‐LM method. First, when the proposed hybrid LSBoost‐LM method was engaged for real‐time extrapolative signal analysis, the results displayed excellent root mean square error precision accuracies compared with two other standards, Bag‐LM and LM methods. As a case in point, the LSBoost‐LM method achieved 2.15, 3.44, 3.33, 1.31, and 2.19 dB RMSE values at different prediction study locations, which are relatively lower compared with the Bag‐LM and standard LM methods that achieved higher RMSE values of 3.38, 3.86, 4.07, 2.28, 3.98 dB and 5.57, 5.52, 5.14, 3.67, 4.56 dB, respectively. Secondly, applying the hybridized model produced up to 93.39% cell area coverage quality and 89.18% fringe cell area coverage quality across the eNodeB study locations. The proposed method can assist practicing RF network planners in realistic cell coverage quality estimation and analysis of related wireless networks. |
| Author | Lee, Cheng‐Chi Isabona, Joseph Imoize, Agbotiname Lucky |
| Author_xml | – sequence: 1 givenname: Joseph surname: Isabona fullname: Isabona, Joseph organization: Federal University Lokoja – sequence: 2 givenname: Agbotiname Lucky orcidid: 0000-0001-8921-8353 surname: Imoize fullname: Imoize, Agbotiname Lucky organization: University of Lagos Akoka – sequence: 3 givenname: Cheng‐Chi orcidid: 0000-0002-8918-1703 surname: Lee fullname: Lee, Cheng‐Chi email: cclee@mail.fju.edu.tw organization: Asia University |
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This paper proposes and engages the Levenberg–Marquardt algorithm method via regression to optimally model and predict real‐time signal strength... This paper proposes and engages the Levenberg–Marquardt algorithm method via regression to optimally model and predict real‐time signal strength values... |
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| SubjectTerms | Algorithms cell coverage area Cellular communication fringe cell coverage Levenberg–Marquardt algorithm LSBoost ensemble Optimization path loss Prediction models Regression models Signal analysis signal path loss model Signal strength Software Wireless networks |
| Title | Hybrid Levenberg–Marquardt and LSBoosting Ensemble Algorithms for Optimal Signal Attenuation Modeling and Coverage Analysis |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fdac.70096 https://www.proquest.com/docview/3192451586 |
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