Narrowband Internet of Things for Non-terrestrial Networks
October 10, 2020 Β· Declared Dead Β· π IEEE Communications Standards Magazine
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Olof Liberg, Stefan Eriksson LΓΆwenmark, Sebastian Euler, BjΓΆrn HofstrΓΆm, Talha Khan, Xingqin Lin, Jonas Sedin
arXiv ID
2010.04906
Category
cs.NI: Networking & Internet
Cross-listed
eess.SP
Citations
54
Venue
IEEE Communications Standards Magazine
Last Checked
5 months ago
Abstract
The Narrowband Internet of Things (NB-IoT) is a cellular access technology developed by the Third Generation Partnership Project (3GPP) to provide wide area connectivity for the Internet of Things. Since its introduction in 3GPP Release 13, NB-IoT has in a few years achieved a remarkable market presence and is currently providing coverage in close to 100 countries. To further extend the reach of NB-IoT and to connect the unconnected, 3GPP Release 17 will study the feasibility of adapting NB-IoT to support non-terrestrial networks (NTNs). In this article, we review the fundamentals of NB-IoT and NTN and explain how NB-IoT can be adapted to support satellite communication through a minimal set of modifications.
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