Deep reinforcement learning paradigm for dense wireless networks in smart cities

R Ali, YB Zikria, BS Kim, SW Kim - Smart cities performability, cognition, & …, 2020 - Springer
Wireless local area networks (WLANs) are widely deployed for Internet-centric data
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …

Deep reinforcement learning paradigm for performance optimization of channel observation–based MAC protocols in dense WLANs

R Ali, N Shahin, YB Zikria, BS Kim, SW Kim - IEEE Access, 2018 - ieeexplore.ieee.org
The potential applications of deep learning to the media access control (MAC) layer of
wireless local area networks (WLANs) have already been progressively acknowledged due …

Reinforcement-learning-enabled massive internet of things for 6G wireless communications

R Ali, I Ashraf, AK Bashir… - IEEE Communications …, 2021 - ieeexplore.ieee.org
Recently, extensive research efforts have been devoted to developing beyond fifth
generation (B5G), also referred to as sixth generation (6G) wireless networks aimed at …

[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 …

Deep reinforcement learning for wireless network

B Sharma, RK Saini, A Singh… - Machine Learning and …, 2020 - Wiley Online Library
The rapid introduction of mobile devices and the growing popularity of mobile applications
and services create unprecedented infrastructure requirements for mobile and wireless …

Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges

Z Xiong, Y Zhang, D Niyato, R Deng… - IEEE Vehicular …, 2019 - ieeexplore.ieee.org
Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …

[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 …

Machine learning techniques and a case study for intelligent wireless networks

H Yang, X Xie, M Kadoch - IEEE Network, 2020 - ieeexplore.ieee.org
With the widespread deployment of wireless technologies and IoT, 5G wireless networks will
support various communication connectivity and services for the huge number of wireless …

Introduction to the special section on deep reinforcement learning for future wireless communication networks

S Gong, DT Hoang, D Niyato… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
We are delighted to introduce the readers to this special section of the IEEE Transactions on
Cognitive Communications and Networking (TCCN), which aims at exploring recent …

Distributed convolutional deep reinforcement learning based OFDMA MAC for 802.11 ax

D Kotagiri, K Nihei, T Li - ICC 2021-IEEE International …, 2021 - ieeexplore.ieee.org
The IEEE 802.11 ax also known as Wi-Fi 6, incorporates multi-user (MU) Orthogonal
Frequency Division Multiple Access (OFDMA) based distributed up-link communication, in …