A Deep Reinforcement Learning-based Contention Window Avoidance Scheme for Distinguishing Network Service Priorities
X Nie, D Wang, Y Zhang, X Pan… - 2024 International …, 2024 - ieeexplore.ieee.org
This study introduces a Deep Reinforcement Learning-based Contention Window
Avoidance Scheme aimed at improving service priority differentiation and network …
Avoidance Scheme aimed at improving service priority differentiation and network …
Enhancing Throughput in Multirate Wireless Networks Using Deep Reinforcement Learning
YS Chen, MH Tsai, CH Ke - … of the 2024 International Conference on …, 2024 - dl.acm.org
In the wireless network environment, random backoff must be performed before packet
transmission to avoid transmission collisions. Since the backoff time depends on the size of …
transmission to avoid transmission collisions. Since the backoff time depends on the size of …
Deep reinforcement learning-based control framework for radio access networks
AH Ahmed, A Elmokashfi - Proceedings of the 28th Annual International …, 2022 - dl.acm.org
Network performance optimization represents one of the major challenges for mobile
network operators, especially with the increasingly use cases that have diverse performance …
network operators, especially with the increasingly use cases that have diverse performance …
Decentralized Deep Reinforcement Learning Approach for Channel Access Optimization
SC da SJ Cruz, FAP de Figueiredo, RAA de Souza - 2024 - researchsquare.com
Abstract The IEEE 802.11 standard's binary exponential back-off (BEB) algorithm is the
prevailing method for tackling the collision avoidance problem. Under the BEB paradigm …
prevailing method for tackling the collision avoidance problem. Under the BEB paradigm …
Deep reinforcement learning-based contention window optimization for IEEE 802.11 networks
This study focuses on optimizing the contention window (CW) in IEEE 802.11 networks
using deep reinforcement learning (DRL) to enhance the effectiveness of the contention …
using deep reinforcement learning (DRL) to enhance the effectiveness of the contention …
Deep reinforcement learning for reducing latency in mission critical services
M Elsayed, M Erol-Kantarci - 2018 IEEE Global …, 2018 - ieeexplore.ieee.org
Next-generation wireless networks will be supporting mission critical services such as safety
related applications of connected autonomous vehicles, and real-time control of medical and …
related applications of connected autonomous vehicles, and real-time control of medical and …
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 …
OSCAR: A Contention Window Optimization Approach Using Deep Reinforcement Learning
C Grasso, R Raftopoulos… - ICC 2023-IEEE …, 2023 - ieeexplore.ieee.org
The contention window (CW) has a significant impact on the efficiency of Wi-Fi networks.
Unfortunately, the basic access method employed by 802.11 networks does not scale well …
Unfortunately, the basic access method employed by 802.11 networks does not scale well …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
Deep Reinforcement Learning based Dynamic Channel Bonding for Wi-Fi Networks
This paper presents a decentralized Deep Reinforcement Learning (DRL)-based dynamic
channel bonding (DCB) algorithm (ie, drlDCB) for Wi-Fi networks. Most existing RL-based …
channel bonding (DCB) algorithm (ie, drlDCB) for Wi-Fi networks. Most existing RL-based …