Mobility Support for Cellular Connected Unmanned Aerial Vehicles: Performance and Analysis
April 12, 2018 Β· Declared Dead Β· π IEEE Wireless Communications and Networking Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sebastian Euler, Helka-Liina Maattanen, Xingqin Lin, Zhenhua Zou, Mattias BergstrΓΆm, Jonas Sedin
arXiv ID
1804.04523
Category
cs.NI: Networking & Internet
Citations
46
Venue
IEEE Wireless Communications and Networking Conference
Last Checked
6 months ago
Abstract
Beyond visual line-of-sight connectivity is key for use cases of unmanned aerial vehicles (UAVs) such as package delivery, infrastructure inspection, and rescue missions. Cellular networks stand ready to support flying UAVs by providing wide-area, quality, and secure connectivity for UAV operations. Ensuring reliable connections in the presence of UAV movements is important for safety control and operations of UAVs. With increasing height above the ground, the radio environment changes. Using terrestrial cellular networks to provide connectivity to the UAVs moving in the sky may face new challenges. In this article, we share some of our findings in mobility support for cellular connected UAVs. We first identify how the radio environment changes with altitude and analyze the corresponding implications on mobility performance. We then present evaluation results to shed light on the mobility performance of cellular connected UAVs. We also discuss potential enhancements for improving mobility performance in the sky.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted