A Wideband Non-Stationary Air-to-Air Channel Model for UAV Communications

Ma, Zhangfeng; Ai, Bo; He, Ruisi; Wang, Gongpu; Niu, Yong; Zhang, Zhong

doi:10.1109/tvt.2019.2961178

Cited by 110 publications

(40 citation statements)

References 37 publications

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“…The work in [23] also developed a reference A2G channel model through statistical simulation, where UAV trajectories are generated by a Gauss-Markov model. For A2A channel modelling, the work in [24] developed a 3D stochastic model and also used a 3D Markov mobility model to capture the movements of the UAV operation. The work in [25] also investigated the characteristics of A2A channel modelling including the large scale path loss, small scale fading along with antenna patterns.…”

Section: A 3d Channel Modelmentioning

confidence: 99%

Unlocking the Potential of 5G and Beyond Networks to Support Massive Access of Ground and Air Devices

Miao

Luo

Min

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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Flying devices, e.g., Unmanned Aerial Vehicles (UAV) and High Altitude Platforms (HAP) are showing great potentials to revolutionise human society with unprecedented efficiency and convenience. 5G and beyond (5GB) networks have been considered as an important infrastructure for supporting flying devices to accomplish mission-critical applications. However, most of the existing research on 5GB networks mainly focuses on technology evolution to support ground devices, paying insufficient attention to the emerging large-scale deployment of flying devices. To fill the gap, this study aims to identify the differences when 5GB networks are used to provide massive access services for the ground devices and their counterpart flying in the air and analyse in which aspects that 5GB should be enhanced to serve flying devices. In detail, a holistic 5GB architecture is presented to support both ground and flying devices. Then, the unique features of flying devices are analysed with a focus on the challenges they bring to 5GB systems. Facing these challenges, we thoroughly investigate the advantages and disadvantages of 5GB key technologies. Furthermore, a case study is presented to demonstrate that flying devices not only create new issues for 5GB design, but also bring new opportunities for 5GB to enhance their service capabilities.

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Section: A 3d Channel Modelmentioning

confidence: 99%

Unlocking the Potential of 5G and Beyond Networks to Support Massive Access of Ground and Air Devices

Miao

Luo

Min

et al. 2021

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…where α ∈ [0, 1] reflects the degree of randomness in the mobility pattern [31]. Also, v and θ denote the asymptotic mean value of velocity and direction for all users when t approaches infinity, respectively.…”

Section: A Network Modelmentioning

confidence: 99%

“…• Uncoordinated state to coordinated state: Last, in case that the UAV moves from an uncoordinated state to a coordinated state, the update equation incorporates the immediate shared reward and the expected discounted reward stored in the shared Q-table. Thus, we have Obtain the initial position of the k th UAV according to (31). 6: end for 7: for each step of episode do 8:…”

Section: B Algorithm Designmentioning

confidence: 99%

Multi-Agent Reinforcement Learning Based 3D Trajectory Design in Aerial-Terrestrial Wireless Caching Networks

Chen

Liao

et al. 2021

IEEE Trans. Veh. Technol.

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“…In mesh networks of aircraft, the ground stations (GSs) are typically located at the airport, which allows the aircraft to directly communicate with Air traffic control for delivering vital control messages. Apart from being either the source or destination, the aircraft also provide relaying services during their landing/take-off, taxiing and holding patterns [1], [27], where these phases are associated with rather diverse channel characteristics, especially the air-to-ground (A2G) or air-toair (A2A) links [28]. The A2G communications and A2A communications rely on the same channel model for their data transmission, but they have different maximum delay.…”

Section: Distance-based Adaptive Coding and Modulationmentioning

confidence: 99%

Semi-Stochastic Aircraft Mobility Modelling for Aeronautical Networks: An Australian Case-Study Based on Real Flight Data

Zhang

Xiang

Liu

et al. 2021

IEEE Trans. Veh. Technol.

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Terrestrial Internet access is gradually becoming the norm across the globe. However, there is a growing demand for Internet access of passenger airplanes. Hence, it is essential to develop aeronautical networks above the clouds. Therefore the conception of an aircraft mobility model is one of the prerequisite for aeronautical network design and optimization. However, there is a paucity of realistic aircraft mobility models capable of generating large-scale flight data. To fill this knowledge-gap, we develop a semi-stochastic aircraft mobility model based on large-scale real historical Australian flights acquired both on June 29th, 2018 and December 25th, 2018, which represent the busiest day and the quietest day of 2018, respectively. The semi-stochastic aircraft mobility model is capable of generating an arbitrary number of flights, which can emulate the specific features of aircraft mobility. The semi-stochastic aircraft mobility model was then analysed and validated both by the physical layer performance and network layer performance in the case study of Australian aeronautical networks, demonstrating that it is capable of reflecting the statistical characteristics of the real historical flights.

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A Wideband Non-Stationary Air-to-Air Channel Model for UAV Communications

Cited by 110 publications

References 37 publications

Unlocking the Potential of 5G and Beyond Networks to Support Massive Access of Ground and Air Devices

Unlocking the Potential of 5G and Beyond Networks to Support Massive Access of Ground and Air Devices

Multi-Agent Reinforcement Learning Based 3D Trajectory Design in Aerial-Terrestrial Wireless Caching Networks

Semi-Stochastic Aircraft Mobility Modelling for Aeronautical Networks: An Australian Case-Study Based on Real Flight Data

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