Enabling Massive Real-Time Applications in IEEE 802.11be Networks
August 06, 2020 Β· Declared Dead Β· π IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Evgeny Avdotin, Dmitry Bankov, Evgeny Khorov, Andrey Lyakhov
arXiv ID
2008.02666
Category
cs.NI: Networking & Internet
Citations
34
Venue
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
Last Checked
6 months ago
Abstract
Next generation Wi-Fi networks are expected to support real-time applications that impose strict requirements on the packet transmission delay and packet loss ratio. Such applications form an essential target for the future Wi-Fi standard, namely IEEE 802.11be, the development process of which started in 2019. A promising way to provide efficient real-time communications in 802.11be networks requires some modification of the uplink OFDMA feature originally introduced in the IEEE 802.11ax amendment to the Wi-Fi standard. This feature allows the access point to reserve channel resources for upcoming urgent transmissions. The paper explains why uplink OFDMA random access of 802.11ax does not perfectly fit the requirements of real-time applications and proposes an easy-to-implement modification of the channel access rules for future 802.11be networks. With extensive simulation, it is shown that this modification together with a new resource allocation algorithm outperforms the existing ways to support real-time applications, especially for a heavy load and a high number of users. In particular, they provide extremely low delays for real-time traffic, while the throughput for non-real-time traffic is reduced insignificantly.
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