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• W4287219375 endingPage "5522" @default.
• W4287219375 startingPage "5522" @default.
• W4287219375 abstract "The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively." @default.
• W4287219375 created "2022-07-25" @default.
• W4287219375 creator A5051849636 @default.
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• W4287219375 date "2022-07-24" @default.
• W4287219375 modified "2023-10-14" @default.
• W4287219375 title "Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach" @default.
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• W4287219375 doi "https://doi.org/10.3390/s22155522" @default.
• W4287219375 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35898026" @default.
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