[HTML][HTML] Applying multi-agent deep reinforcement learning for contention window optimization to enhance wireless network performance

CH Ke, L Astuti - ICT Express, 2023 - Elsevier
This paper investigates the Contention Window (CW) optimization problem in multi-agent
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

N Zhao, YC Liang, D Niyato, Y Pei… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
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 …

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 …

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 …

Enhanced-SETL: A Multi-Variable Deep Reinforcement Learning Approach for Contention Window Optimization in Dense Wi-Fi Networks

YH Tu, EC Lin, CH Ke, YW Ma - Computer Networks, 2024 - Elsevier
In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-
SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning …

[HTML][HTML] 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 …

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 …

[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 …

Adaptive contention window design using deep Q-learning

A Kumar, G Verma, C Rao, A Swami… - ICASSP 2021-2021 …, 2021 - ieeexplore.ieee.org
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 …

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 …