Estimating the number of receiving nodes in 802.11 networks via machine learning techniques
D Del Testa, M Danieletto… - 2016 IEEE Global …, 2016 - ieeexplore.ieee.org
Nowadays, most mobile devices are equipped with multiple wireless interfaces, causing an
emerging research interest in device to device (D2D) communication: the idea behind the …
emerging research interest in device to device (D2D) communication: the idea behind the …
[HTML][HTML] AI-enabled framework for mobile network experimentation leveraging ChatGPT: Case study of channel capacity calculation for η-µ fading and co-channel …
Artificial intelligence has been identified as one of the main driving forces of innovation in
state-of-the-art mobile and wireless networks. It has enabled many novel usage scenarios …
state-of-the-art mobile and wireless networks. It has enabled many novel usage scenarios …
A survey of machine learning algorithms for 6G wireless networks
The primary focus of Artificial Intelligence/Machine Learning (AI/ML) integration within the
wireless technology is to reduce capital expenditures, optimize network performance, and …
wireless technology is to reduce capital expenditures, optimize network performance, and …
Can Wi-Fi 7 support real-time applications? On the impact of multi link aggregation on latency
Multi Link Aggregation (MLA) is a feature likely to be introduced in Wi-Fi 7, the next-
generation of Wi-Fi, which will be based on the IEEE 802.11 be specifications. MLA will …
generation of Wi-Fi, which will be based on the IEEE 802.11 be specifications. MLA will …
Data sets for machine learning in wireless communications and networks
C Fischione, M Chafii, Y Deng… - IEEE Communications …, 2023 - ieeexplore.ieee.org
The articles in this special section focus on the role of data sets for the evolution of the
telecommunication industry in the 5G and 6G era. In 5G and 6G, many new services are …
telecommunication industry in the 5G and 6G era. In 5G and 6G, many new services are …
Applying deep reinforcement learning to improve throughput and reduce collision rate in IEEE 802.11 networks
Abstract The effectiveness of Wi-Fi networks is greatly influenced by the optimization of
contention window (CW) parameters. Unfortunately, the conventional approach employed …
contention window (CW) parameters. Unfortunately, the conventional approach employed …
Path-link graph neural network for IP network performance prediction
Dynamic resource provisioning and quality assurance for the plethora of end-to-end slices
running over 5G and B5G networks require advanced modeling capabilities. Graph Neural …
running over 5G and B5G networks require advanced modeling capabilities. Graph Neural …
An empirical analysis of IEEE 802.11 ax
S Muhammad, J Zhao, HH Refai - … International Conference on …, 2021 - ieeexplore.ieee.org
An empirical analysis of the newly released standard IEEE 802.11 ax, widely known as Wi-Fi
6, is presented in this paper. Several tests were conducted to evaluate key performance …
6, is presented in this paper. Several tests were conducted to evaluate key performance …
On packet loss rates in modern 802.11 networks
RK Sheshadri, D Koutsonikolas - IEEE INFOCOM 2017-IEEE …, 2017 - ieeexplore.ieee.org
The knowledge of link packet loss rates (PLRs) at different PHY layer configurations is vital
for a number of wireless network optimization schemes. However, the very large number of …
for a number of wireless network optimization schemes. However, the very large number of …
Practical machine learning-based rate adaptation solution for Wi-Fi NICs: IEEE 802.11 ac as a case study
CY Li, SC Chen, CT Kuo… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Many rate adaptation (RA) solutions have been proposed for legacy Wi-Fi standards.
However, these solutions lack extensibility, and cannot therefore be easily applied to new Wi …
However, these solutions lack extensibility, and cannot therefore be easily applied to new Wi …