A New Method for Radio Wave Propagation Prediction Based on BP-Neural Network and Path Loss Model

Wang, Yirong; Huang, Chen; Liang, Man; Hu, Jing; Song, Tiecheng

doi:10.1109/kst48564.2020.9059383

Cited by 2 publications

(1 citation statement)

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“…As long as there are enough measurements, ANNs can be used for modeling any scenario, such as blending [80][81][82], indoor [83], suburban [84,85], and street scenarios [86]. The Back Propagation NN (BPNN) is used to model the blending [19,87] and cabin scenarios [88]. In particular, Ojo et al developed two models based on the radial basis function NN (RBFNN) and the multilayer perception NN (MLPNN) by using the measured data as input variables for blending scenarios [89].…”

Section: Scenario Evaluation Parameters Conclusionmentioning

confidence: 99%

The Current Progress and Future Prospects of Path Loss Model for Terrestrial Radio Propagation

Wang,

Hao,

Yang

2023

Electronics

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The radio channel model is a major factor supporting the whole life cycle of the terrestrial radio system, including the demonstration, design, validation, operation, and so on. To improve the spectrum sharing and spectral efficiency in terrestrial radio services, we analyze three types of path loss models in detail: deterministic, empirical, and semi-empirical models, to meet the requirements of path loss modeling for supporting traditional band expansion and reuse. Then, we conduct a comparative analysis based on the characteristics of the current models. Furthermore, a preview of the future terrestrial path loss modeling methods is provided, including intelligent modeling processes and multi-model hybridization methods. Finally, we look forward to the potential technology that can be used in future wireless communication, such as terahertz communication, reconfigurable intelligent surface technology, and integrated communication and sensing technology. The above research can provide a reference for the development of terrestrial radio channel modeling, promoting the technologies of terrestrial channel modeling. We hope this paper will stimulate more interest in modeling terrestrial radio channels.

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