Cellular Connectivity for UAVs: Network Modeling, Performance Analysis and Design Guidelines
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Abstract (arXiv)
The growing use of aerial user equipments (UEs) in various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground stations. Key questions that need to be addressed when planning the deployment of aerial UEs are whether the cellular network is a suitable candidate for enabling such connectivity, and how the inclusion of aerial UEs might impact the overall network efficiency. This paper provides an in-depth analysis of user and network level performance of a cellular network that serves both unmanned aerial vehicles (UAVs) and ground users in the downlink. Our results show that the favorable propagation conditions that UAVs enjoy due to their height often backfire on them, as the increased co-channel interference received from neighboring ground BSs is not compensated by the improved signal strength. When compared with a ground user in an urban area, our analysis shows that a UAV flying at 100 meters can experience a throughput decrease of a factor 10 and a coverage drop from 76% to 30%. Motivated by these findings, we develop UAV and network based solutions to enable an adequate integration of UAVs into cellular networks. In particular, we show that an optimal tilting of the UAV antenna can increase their coverage and throughput from 23% to 89% and from 3.5 b/s/Hz to 5.8 b/s/Hz, respectively, outperforming ground UEs. Furthermore, our findings reveal that depending on UAV altitude, the aerial user performance can scale with respect to the network density better than that of a ground user. Finally, our results show that network densification and the use of micro cells limit UAV performance. While UAV usage has the potential to increase area spectral efficiency (ASE) of cellular networks with moderate number of cells, they might hamper the development of future ultra dense networks.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.
5 papers.
- 2021Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learningcited by 3
- 2021Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraintscited by 3
- 2023Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluationcited by 3
- 2022A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interferencecited by 2
- 2024PCA-Featured Transformer for Jamming Detection in 5G UAV Networkscited by 0
- 2024PCA-Featured Transformer for Jamming Detection in 5G UAV Networks19 Dec 2024
- 2023Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation3 Mar 2023
- 2022A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference16 Jul 2022
- 2021Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning9 Apr 2021
- 2021Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints3 Apr 2021