Latency fairness optimization on wireless networks through deep reinforcement learning
M López-Sánchez, A Villena-Rodríguez… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
In this paper, we propose a novel deep reinforcement learning (DRL) framework to
maximize user fairness in terms of delay. To this end, we devise a new version of the …
maximize user fairness in terms of delay. To this end, we devise a new version of the …
SmartLA: Reinforcement learning-based link adaptation for high throughput wireless access networks
R Karmakar, S Chattopadhyay… - Computer Communications, 2017 - Elsevier
High throughput wireless standards based on IEEE 802.11 n and IEEE 802.11 ac have been
developed and released within the last few years as new amendments over the …
developed and released within the last few years as new amendments over the …
An improved communication resource allocation strategy for wireless networks based on deep reinforcement learning
T Xu, M Zhao, X Yao, Y Zhu - Computer Communications, 2022 - Elsevier
With the advent of 5G networks, user demand for high-speed, low-latency, and high-
reliability services continues to grow. When traditional communication technologies cannot …
reliability services continues to grow. When traditional communication technologies cannot …
Distributed Q-learning based-decentralized resource allocation for future wireless networks
The next generation (5G) wireless network is expected to support the fourth industrial
revolution, combining heightened data transfer speeds and processing power. Despite …
revolution, combining heightened data transfer speeds and processing power. Despite …
Deep reinforcement learning for wireless network
The rapid introduction of mobile devices and the growing popularity of mobile applications
and services create unprecedented infrastructure requirements for mobile and wireless …
and services create unprecedented infrastructure requirements for mobile and wireless …
Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks
Underlay Cognitive Radio (CR) systems were introduced to resolve the issue of spectrum
scarcity in wireless communication. In CR systems, an unlicensed Secondary Transmitter …
scarcity in wireless communication. In CR systems, an unlicensed Secondary Transmitter …
Deep Reinforcement Learning algorithms for Low Latency Edge Computing Systems
K Kumaran, E Sasikala - 2023 3rd International conference on …, 2023 - ieeexplore.ieee.org
Nowadays, due to the increase of technological development in smart devices, more
computational capabilities are needed with better performance. The maximization of the …
computational capabilities are needed with better performance. The maximization of the …
Simple reinforcement learning based contention windows adjustment for IEEE 802.11 networks
K Sanada, H Hatano, K Mori - 2023 IEEE 20th consumer …, 2023 - ieeexplore.ieee.org
This paper proposes a simple reinforcement learning-based CW adjustment for IEEE 802.11
Networks. In the proposed scheme, each node finds an optimal value of CWmin for networks …
Networks. In the proposed scheme, each node finds an optimal value of CWmin for networks …
A deep reinforcement learning based approach for channel aggregation in IEEE 802.11 ax
Channel aggregation (CA) is proposed in IEEE 802.11 ax to allow wireless users to
aggregate multiple available channels, either contiguous or non-contiguous, to improve the …
aggregate multiple available channels, either contiguous or non-contiguous, to improve the …
A Q-learning approach with collective contention estimation for bandwidth-efficient and fair access control in IEEE 802.11 p vehicular networks
Vehicular ad hoc networks (VANETs) are wireless networks formed of moving vehicle
stations, that enable safety-related packet exchanges among them. Their infrastructure-less …
stations, that enable safety-related packet exchanges among them. Their infrastructure-less …