Wi-Fi meets ML: A survey on improving IEEE 802.11 performance with machine learning
Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant
position in providing Internet access thanks to their freedom of deployment and configuration …
position in providing Internet access thanks to their freedom of deployment and configuration …
Multi-agent reinforcement learning-based distributed channel access for next generation wireless networks
Z Guo, Z Chen, P Liu, J Luo, X Yang… - IEEE Journal on …, 2022 - ieeexplore.ieee.org
In the next generation wireless networks, more applications will emerge, covering virtual
reality movies, augmented reality, holographic three-dimensional telepresence, haptic …
reality movies, augmented reality, holographic three-dimensional telepresence, haptic …
Introducing reinforcement learning in the Wi-Fi MAC layer to support sustainable communications in e-Health scenarios
The crisis of energy supplies has led to the need for sustainability in technology, especially
in the Internet of Things (IoT) paradigm. One solution is the integration of passive …
in the Internet of Things (IoT) paradigm. One solution is the integration of passive …
Link scheduling using graph neural networks
Efficient scheduling of transmissions is a key problem in wireless networks. The main
challenge stems from the fact that optimal link scheduling involves solving a maximum …
challenge stems from the fact that optimal link scheduling involves solving a maximum …
[HTML][HTML] Applying multi-agent deep reinforcement learning for contention window optimization to enhance wireless network performance
This paper investigates the Contention Window (CW) optimization problem in multi-agent
scenarios, where the fully cooperative among mobile stations is considered. A partially …
scenarios, where the fully cooperative among mobile stations is considered. A partially …
Graph-based algorithm unfolding for energy-aware power allocation in wireless networks
We develop a novel graph-based trainable framework to maximize the weighted sum energy
efficiency (WSEE) for power allocation in wireless communication networks. To address the …
efficiency (WSEE) for power allocation in wireless communication networks. To address the …
Power allocation for wireless federated learning using graph neural networks
We propose a data-driven approach for power allocation in the context of federated learning
(FL) over interference-limited wireless networks. The power policy is designed to maximize …
(FL) over interference-limited wireless networks. The power policy is designed to maximize …
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 …
ML-aided power allocation for tactical MIMO
We study the problem of optimal power allocation in single-hop multi-antenna ad-hoc
wireless networks. A standard technique to solve this problem involves optimizing a tri …
wireless networks. A standard technique to solve this problem involves optimizing a tri …
Wireless LAN performance enhancement using double deep Q-networks
K Asaf, B Khan, GY Kim - Applied Sciences, 2022 - mdpi.com
Due to the exponential growth in the use of Wi-Fi networks, it is necessary to study its usage
pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access …
pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access …