Contention window optimization in IEEE 802.11 ax networks with deep reinforcement learning
W Wydmański, S Szott - 2021 IEEE wireless communications …, 2021 - ieeexplore.ieee.org
The proper setting of contention window (CW) values has a significant impact on the
efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is …
efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is …
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 …
Collision avoidance in IEEE 802.11 DCF using a reinforcement learning method
CK Lee, SH Rhee - 2020 International conference on …, 2020 - ieeexplore.ieee.org
In IEEE 802.11 networks, wireless stations try to avoid congestions using the random backoff
algorithm in distributed coordination function (DCF); however, for a large number of nodes …
algorithm in distributed coordination function (DCF); however, for a large number of nodes …
Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11 ah MAC Layer
The IEEE 802.11 ah standard is introduced to address the growing scale of internet of things
(IoT) applications. To reduce contention and enhance energy efficiency in the system, the …
(IoT) applications. To reduce contention and enhance energy efficiency in the system, the …
Meta-gating framework for fast and continuous resource optimization in dynamic wireless environments
With the great success of deep learning (DL) in image classification, speech recognition,
and other fields, more and more studies have applied various neural networks (NNs) to …
and other fields, more and more studies have applied various neural networks (NNs) to …
Optimal trajectory learning for UAV-mounted mobile base stations using RL and greedy algorithms
AB Bhandarkar, SK Jayaweera - 2021 17th International …, 2021 - ieeexplore.ieee.org
This paper designs Artificial Intelligence (AI) method, to determine an optimal trajectory for
an Unmanned Aerial Vehicle (UAV) mounted mobile base station to maximize its coverage …
an Unmanned Aerial Vehicle (UAV) mounted mobile base station to maximize its coverage …
Mobility-aware trajectory design for aerial base station using deep reinforcement learning
G Hao, W Ni, H Tian, L Cao - 2020 International Conference on …, 2020 - ieeexplore.ieee.org
In this paper, an unmanned aerial vehicle (UAV) assisted wireless network is investigated,
where an aerial base station (ABS) is deployed for serving both ground and aerial users …
where an aerial base station (ABS) is deployed for serving both ground and aerial users …
Joint server selection, cooperative offloading and handover in multi-access edge computing wireless network: A deep reinforcement learning approach
Multi-access edge computing (MEC) is the key enabling technology that supports compute-
intensive applications in 5G networks. By deploying powerful servers at the edge of wireless …
intensive applications in 5G networks. By deploying powerful servers at the edge of wireless …
Multi-UAV dynamic wireless networking with deep reinforcement learning
This letter investigates a novel unmanned aerial vehicle (UAV)-enabled wireless
communication system, where multiple UAVs transmit information to multiple ground …
communication system, where multiple UAVs transmit information to multiple ground …
Cfx: contention-free channel access for IEEE 802.11 ax
K Lee, D Kim - Sensors, 2022 - mdpi.com
Orthogonal frequency-division multiple access (OFDMA) has attracted great attention as a
key technology for uplink enhancement for Wi-Fi, since it can effectively reduce network …
key technology for uplink enhancement for Wi-Fi, since it can effectively reduce network …