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Journal article
On the application of generalized linear mixed models for predicting path loss in LTE networks
EURASIP journal on advances in signal processing, Vol.2023(1), pp.6-13
01/11/2023
Web of Science ID: WOS:000910903800001
Abstract
To meet the ever-growing demand for higher data rates, accurate channel models are needed for optimal design and deployment of mobile wireless networks. This work proposes a new method for addressing path loss modeling at 800 MHz of suburban environment based on field measurements. Using generalized linear mixed models, we develop a new statistical model that accounts for the autocorrelation among measurements at the same distance at different times. The proposed method allows linear, quadratic, and cubic relationship forms between the path loss measurements and the natural logarithm of the distance, which is almost unexplored as existing models use a straight line relationship. A comparison study consists of comparing nine propagation models in terms of the mean absolute prediction error. The new model performs over 30% better than the existing models for the considered measurements. We also show that a cubic relationship form between path loss measurements and the logarithm of distance could be more suitable than a straight line form for prediction purposes. The results show that the generalized linear mixed models significantly improve the prediction power regardless of the form of the model (linear, quadratic, or cubic).
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- Title
- On the application of generalized linear mixed models for predicting path loss in LTE networks
- Publication Details
- EURASIP journal on advances in signal processing, Vol.2023(1), pp.6-13
- Resource Type
- Journal article
- Format
- pdf
- Copyright
- © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Identifiers
- WOS:000910903800001; 99380171795906600
- Academic Unit
- Dr. Muhammad Harunur Rashid Department of Electrical and Computer Engineering; Hal Marcus College of Science and Engineering ; Mathematics and Statistics
- Language
- English