WNL members at Engineering and Telecommunications (En&T) Conference
This week, November 24-25, the VIII international conference «Engineering and Telecommunication – En&T-2021» is held. En&T-2021 provides a platform for discussing innovative ideas in the field of information and communication technologies, as well as their application in various industries and manufacturing. Members of the WNL team present their works at this conference on Thursday, 25 November 2021. Feel free to join the conference in Zoom and listen to our talks!
Please, see http://2021.en-t.info for information.
The first talk from our team is “A Study of the Impact of the Contention Window on the Performance of IEEE 802.11bd Networks with Channel Bonding”, presented by Viktor Torgunakov. Vehicle-To-Everything (V2X) networks are actively developing, and most of them are currently based on the IEEE 802.11p standard. However, to fulfill more stringent requirements of modern V2X applications, the IEEE has launched the development of a new IEEE 802.11bd standard that introduces a channel bonding technique for V2X. The study compares different channel bonding techniques that can be used in IEEE 802.11bd networks. The authors show the best technique that provides the lowest frame transmission delays and packet loss ratio, and they also reveal the optimum contention window size for this technique.
The second talk “Enabling Synchronous Uplink NOMA in Wi-Fi Networks” is presented by Grigory Korolev. Non-Orthogonal Multiple Access (NOMA) is a promising technology for future Wi-Fi. In uplink NOMA, stations with different channel conditions transmit simultaneously at the same frequency by splitting the signal by power level. The paper presents a data transmission mechanism in Wi-Fi networks that enables synchronous uplink NOMA, where multiple stations start data transmission to the access point simultaneously. With simulation, it is shown that the developed mechanism can double the total throughput and geometric mean throughput.
The last talk from our team “Are Neural Networks the Best Way for Encrypted Traffic Classification?” is presented by Danil Shamsimukhametov. The answer to the question in the title is “currently, no, but neural networks classify traffic surprisingly well, even encrypted one”. This paper looks deep inside the encrypted traffic of widely used applications and points out the essential peculiarities of the security protocols. The paper identifies the plain-text data in the encrypted traffic that hints to the neural network algorithms. Based on the findings, the paper describes a spot-on, lightweight, and learning-free method that can easily classify the traffic encrypted with current security protocols. Finally, the paper predicts that the next versions of security protocols may conceal more data and complicate traffic classification.