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Prediction of path loss in coastal and vegetative environments with deep learning at 5G sub-6 GHz

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Abstract

Path loss prediction is quite important for the network performance of the wireless sensors, quality of cellular communication-based link budget, and optimization of coverage planning in mobile networks. With the development of 5G technology, even though different log-distance path loss models are generated for these, new-developed methods are required to make models more flexible and accurate for complex environments. In this study, for different coastal terrains (air-dry sand, wet sand, small pebble, big pebble) and various vegetable areas (pine, orange, cherry, and walnut), the principle and procedure of deep learning-based path loss prediction are provided in 3.5 GHz, 3.8 GHz, and 4.2 GHz in the 5G frequency zone, as a novelty. For this, recurrent neural network (RNN) and long short-term memory (LSTM) methods are proposed. The test sample number is 240 since 20% of all datasets (1200) are test data. In general, path loss for coastal terrains is higher than path loss for vegetation areas with an average of 5 dB. For both coastal terrains and vegetation areas, the recurrent neural network method predicts better than the long short-term memory method. Consequently, for both coastal terrains and vegetation areas, RNN models with R2 values of 0.9677 and 0.9042, respectively, are preferred.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Isparta University of Applied Sciences, Isparta, Turkey

    Kiyas Kayaalp

  2. Department of Electronics and Automation, Burdur Mehmet Akif Ersoy University, Burdur, Turkey

    Sedat Metlek

  3. Department of Mechatronics Engineering, Isparta University of Applied Sciences, Isparta, Turkey

    Abdullah Genc

  4. Department of Computer Technologies and Information Systems, Burdur Mehmet Akif Ersoy University, Burdur, Turkey

    Habib Dogan

  5. Department of Computer Technology, Isparta University of Applied Sciences, Isparta, Turkey

    İbrahim Bahadir Basyigit

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  1. Kiyas Kayaalp
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  5. İbrahim Bahadir Basyigit
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Correspondence to İbrahim Bahadir Basyigit.

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Kayaalp, K., Metlek, S., Genc, A. et al. Prediction of path loss in coastal and vegetative environments with deep learning at 5G sub-6 GHz. Wireless Netw 29, 2471–2480 (2023). https://doi.org/10.1007/s11276-023-03285-w

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