Path Loss Estimation of Air-to-Air Channels for FANETs over Rugged Terrains

Cabuk, Umut Can; Tosun, Mustafa; Jacobsen, Rune Hylsberg; Dağdeviren, Orhan

doi:10.1109/siu49456.2020.9302160

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…In [46], already known air-to-air channel models were revisited and path loss-related simulations were only performed for two UAVs. The work was a precursor initiative of FANET algorithms development.…”

Section: Uav and Usv Network Enhancementsmentioning

confidence: 99%

“…However, most of the employed models (e.g., Egli, Okumura, SUI) have some limitations related to the height and distance ranges. As compared to [46], in our paper, we used UAV propagation models adapted to real-case scenarios. For example, we used the 3GPP model to highlight the propagation losses regarding communication between ground users and UAVs and between UAVs themselves when they are not in the line-of-sight condition, under a height of 60 m. Moreover, the height and distance ranges were broader and closer to real environments.…”

Section: Uav and Usv Network Enhancementsmentioning

confidence: 99%

“…In contrast to [44] where a path planning algorithm based on environmental parameters (wind speed and direction) for UAVs is presented, we propose an architecture and a positioning algorithm based on the communication parameters related to path loss that allows the positioning of UVs to deploy an entire network infrastructure based on UAVs and USVs. With respect to [45,46], our algorithm uses all types of UAV channels (A2A, A2G equivalent to G2A) and USV channels (OW, S2S, A2S). In addition, our algorithm does not employ only one UAV as in [45].…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…In addition, our algorithm does not employ only one UAV as in [45]. In comparison with [46] where the air-to-air channel models are investigated, we used UAV propagation models adapted to a wider range of applications. For example, concerning UAV-related channels, besides the air-to-air channel, we analysed an air-to-ground channel (having the same properties as the ground-to-air channel).…”

Section: Comparison With Other Workmentioning

confidence: 99%

See 3 more Smart Citations

Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles

Drăgulinescu

Halunga

Zamfirescu

2021

Sensors

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Currently, the use of unmanned vehicles, such as drones, boats and ships, in monitoring tasks where human presence is difficult or even impossible raises several issues. Continuous efforts to improve the autonomy of such vehicles have not solved all aspects of this issue. In an Internet of Unmanned Vehicles (IoUV) environment, the idea of replacing the static wireless infrastructure and reusing the mobile monitoring nodes in different conditions would converge to a dynamic solution to assure data collection in areas where there is no infrastructure that ensures Internet access. The current paper fills a significant gap, proposing an algorithm that optimises the positions of unmanned vehicles such that an ad hoc network is deployed to serve specific wireless sensor networks that have no other Internet connectivity (hilly/mountainous areas, Danube Delta) and must be connected to an Internet of Things (IoT) ecosystem. The algorithm determines the optimum positions of UV nodes that decrease the path losses below the link budget threshold with minimum UV node displacement compared to their initial coordinates. The algorithm was tested in a rural scenario and 3rd Generation Partnership Project (3GPP), free space and two-ray propagation models. The paper proposes another type of network, a Flying and Surface Ad Hoc Network (FSANET), a concept which implies collaboration and coexistence between unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) and several use cases that motivate the need for such a network.

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Section: Uav and Usv Network Enhancementsmentioning

confidence: 99%

Section: Uav and Usv Network Enhancementsmentioning

confidence: 99%

Section: Comparison With Other Workmentioning

confidence: 99%

Section: Comparison With Other Workmentioning

confidence: 99%

See 2 more Smart Citations

Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles

Drăgulinescu

Halunga

Zamfirescu

2021

Sensors

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“…It shows that the excess fading loss (EL) and close-in free space (CI) can apply as a simple tool for the A2A channel model. Afterward, the path loss of the A2A channel for the flying ad hoc network (FANET) was examined by using the SUI model [30]. The simulation data considered path loss for flying over rugged terrains.…”

Section: Introductionmentioning

confidence: 99%

Empirical Path Loss Channel Characterization Based on Air‐to‐Air Ground Reflection Channel Modeling for UAV‐Enabled Wireless Communications

Supramongkonset

Duangsuwan

Maw

et al. 2021

Wireless Communications and Mobile Computing

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The purpose of this work was to investigate the air-to-air channel model (A2A-CM) for unmanned aerial vehicle- (UAV-) enabled wireless communications. Specifically, a low-altitude small UAV needs to characterize the propagation mechanisms from ground reflection. In this paper, the empirical path loss channel characterizations of A2A ground reflection CM based on different scenarios were presented by comparing the wireless communication modules for UAVs. Two types of wireless communication modules both WiFi 2.4 GHz and LoRa 868 MHz frequency were deployed to study the path loss channel characterization between Tx-UAV and Rx-UAV. To investigate the path loss, three types of experimental channel models, such as CM1 grass floor, CM2 soil floor, and CM3 rubber floor, were considered under the ground reflection condition. The analytical A2A Two-Ray (A2AT-R) model and the modified Log-Distance model were simulated to compare the correlation with the measurement data. The measurement results in the CM3 rubber floor scenario showed the impact from the ground reflection at 1 m to 3 m Rx-UAV altitudes both 2.4 GHz and 868 MHz which was converged to the A2AT-R model and related to the modified Log-Distance model above 3 m. It clear that there is no ground reflection effect from the CM1 grass floor and CM2 soil floor. This work showed that the analytical A2AT-R model and the modified Log-Distance model can deploy to model the path loss of A2A-CM by using WiFi and LoRa wireless modules.

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A Survey of Path Loss Prediction and Channel Models for Unmanned Aerial Systems for System-Level Simulations

Moraitis

Psychogios

Panagopoulos

2023

Sensors

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Unmanned aerial systems (UAS) have recently gained popularity, and they are envisioned as an integral parts of the current and future wireless and mobile-radio networks. Despite the exhaustive research on air-to-ground channels, there are insufficient studies, experimental campaigns and general channel models related to air-to-space (A2S) and air-to-air (A2A) wireless links. This paper presents a comprehensive review of the available channel models and path-loss prediction for A2S and A2A communications. Specific case studies attempting to extend current models’ parameters and provide important knowledge of the channel behavior in combination with UAV flight characteristics are also provided. A time-series rain-attenuation synthesizer is also presented that describes quite accurately the impact of the troposphere at frequencies above 10 GHz. This specific model can be also applied to both A2S and A2A wireless links. Finally, scientific challenges and gaps that can be used for future research on the upcoming 6G networks are highlighted.

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Path Loss Estimation of Air-to-Air Channels for FANETs over Rugged Terrains

Cited by 3 publications

References 10 publications

Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles

Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles

Empirical Path Loss Channel Characterization Based on Air‐to‐Air Ground Reflection Channel Modeling for UAV‐Enabled Wireless Communications

A Survey of Path Loss Prediction and Channel Models for Unmanned Aerial Systems for System-Level Simulations

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