Unlicensed LTE/WiFi Coexistence: Is LBT Inherently Fairer Than CSAT?
November 19, 2015 Β· Declared Dead Β· π 2016 IEEE International Conference on Communications (ICC)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Cristina Cano, Douglas J. Leith
arXiv ID
1511.06244
Category
cs.NI: Networking & Internet
Citations
103
Venue
2016 IEEE International Conference on Communications (ICC)
Last Checked
4 months ago
Abstract
Ensuring fair co-existence between unlicensed LTE and WiFi networks is currently of major concern to both cellular operators and WiFi providers. Two main unlicensed LTE approaches currently being discussed, namely Carrier Sense Adaptive Transmission (CSAT) and Listen Before Talk (LBT). While these mechanisms differ in their compatibility with existing LTE specifications and regulatory compliance in different countries, they also use fundamentally different approaches to access the channel. Nevertheless, we show in this article that when optimally configured both approaches are capable of providing the same level of fairness to WiFi and that the choice between CSAT and LBT is solely driven by the LTE operator's interests.
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