Channel Analysis for 3.5 GHz Frequency in Airport

,; Macedo, Alex; Costa, Thiago; de Matos, Edemir; ,; ,; Castro Eras, Leslye; ,; de Araujo, Jasmine; ,; Gonzalez Castellanos, Pedro Vladimir; ,; Barros, Fabricio; ,

doi:10.14209/jcis.2023.13

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…This path loss model is referenced as the Free Space Path Loss (

) provided in the equation and parameterized by the model parameter “ n ”, also known as the Path Loss Exponent (PLE) [ 41 ]. In this model, the PLE is modeled through MMSE [ 40 ], aiming to minimize the standard deviation (

) between the Path Loss and the measured data.…”

Section: Empirical Propagation Modelsmentioning

confidence: 99%

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments

Barbosa,

Cruz,

Macedo

et al. 2024

Sensors

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The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter’s height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of 3.86 dB and standard deviation (SD) of 3.8614 dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure.

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“…This path loss model is referenced as the Free Space Path Loss (

) between the Path Loss and the measured data.…”

Section: Empirical Propagation Modelsmentioning

confidence: 99%

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments

Barbosa,

Cruz,

Macedo

et al. 2024

Sensors

View full text Add to dashboard Cite

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Analysis of 5G Technology in Airports: Requirements, Challenges, and Use Cases Evaluation

Cantizani-Estepa,

Vera-Soto,

Baena

et al. 2023

IEEE Access

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The purpose of this paper is to fill a gap in the existing literature about the general analysis of airport activities and detailed specifications for 5G implementation. The gap spans from overgeneralized studies to highly specific ones as will be mentioned in the paper. This research aims to bridge this gap by identifying the requirements of 5G implementation in various airport use cases, proposing detailed 5G solutions for these cases. As is well known, 5G is the Fifth Generation of cellular communications, aiming to provide high-throughput high-reliability connectivity to greatly diversified scenarios. With this objective, it shall act as a viable solution for environments as complex as an airport terminal, whose dynamic work cycle includes a wide range of diversified activities. As such, 5G capacities are assessed, identifying those airport processes that can benefit from its application as well as the general requirements of the network deployed for such scenarios. From the identified use cases, the streaming of high quality video for aircraft visual inspection as well as the monitoring of luggage trolleys via an IoT system are developed and evaluated, showing the capabilities of the approach.

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Channel Analysis for 3.5 GHz Frequency in Airport

Cited by 2 publications

References 14 publications

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments

Analysis of 5G Technology in Airports: Requirements, Challenges, and Use Cases Evaluation

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