[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 …
Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …
costs, which have been considered to be a promising technique in the next-generation …
Intelligent resource allocation method for wireless communication networks based on deep learning techniques
H Hui - Journal of Sensors, 2021 - Wiley Online Library
In this paper, a deep learning approach is used to conduct an in‐depth study and analysis of
intelligent resource allocation in wireless communication networks. Firstly, the concepts …
intelligent resource allocation in wireless communication networks. Firstly, the concepts …
Reinforcement learning-based Wi-Fi contention window optimization
SJ Sheila de Cássia, MA Ouameur… - Journal of …, 2023 - jcis.emnuvens.com.br
The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal.
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …
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 …
Reinforcement Learning-based Wi-Fi Contention Window Optimization
SCS Cruz, MA Ouameur, FAP de Figueiredo - 2022 - preprints.org
The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal.
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …
Reinforcement learning based multi-parameter joint optimization in dense multi-hop wireless networks
J Lei, D Tan, X Ma, Y Wang - Ad Hoc Networks, 2024 - Elsevier
Abstract Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) restricts the channel
utilization efficiency although it always is regarded as a promising distributed channel …
utilization efficiency although it always is regarded as a promising distributed channel …
[PDF][PDF] Intelligent CW Selection Mechanism Based on Q-Learning (MISQ).
N Zerguine, M Mostefai, Z Aliouat… - Ingénierie des Systèmes …, 2020 - researchgate.net
Accepted: 3 December 2020 Mobile ad hoc networks (MANETs) consist of self-configured
mobile wireless nodes capable of communicating with each other without any fixed …
mobile wireless nodes capable of communicating with each other without any fixed …
Adaptive contention window design using deep Q-learning
We study the problem of adaptive contention window (CW) design for random-access
wireless networks. More precisely, our goal is to design an intelligent node that can …
wireless networks. More precisely, our goal is to design an intelligent node that can …
Deployment of Unmanned Aerial Vehicles in Next-Generation Wireless Communication Network using Multi-Agent Reinforcement Learning
To address the challenges posed by a large number of disaster-waiver-affected users and
the complexities of scaling centralized algorithms for rapidly restoring emergency …
the complexities of scaling centralized algorithms for rapidly restoring emergency …