Applications of Deep Reinforcement Learning in Wireless Networks-A Recent Review

A Archi, HA Saadi, S Mekaoui - 2023 2nd International …, 2023 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent
years for future wireless networks. They can overcome the ever-increasing challenges of …

A novel machine learning-based handover scheme for hybrid LiFi and WiFi networks

X Wu, DC O'Brien - 2020 IEEE Globecom Workshops (GC …, 2020 - ieeexplore.ieee.org
Combining the high area spectrum efficiency of light fidelity (LiFi) and the ubiquitous
coverage of wireless fidelity (WiFi), hybrid LiFi and WiFi networks have drawn increasing …

Toward energy-efficient federated learning over 5g+ mobile devices

D Shi, L Li, R Chen, P Prakash, M Pan… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
The continuous convergence of machine learning algorithms, 5G and beyond (5G+)
wireless communications, and artificial intelligence (AI) hardware implementation hastens …

Deep reinforcement learning for 5G networks: Joint beamforming, power control, and interference coordination

FB Mismar, BL Evans… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The fifth generation of wireless communications (5G) promises massive increases in traffic
volume and data rates, as well as improved reliability in voice calls. Jointly optimizing …

Federated reinforcement learning-based resource allocation in D2D-enabled 6G

Q Guo, F Tang, N Kato - IEEE Network, 2022 - ieeexplore.ieee.org
The current 5G and conceived 6G era with ultrahigh density, ultra-high frequency bandwidth,
and ultra-low latency can support emerging applications like Extended Reality (XR), Vehicle …

Deep reinforcement learning-based network slicing for beyond 5G

K Suh, S Kim, Y Ahn, S Kim, H Ju, B Shim - IEEE Access, 2022 - ieeexplore.ieee.org
With the advent of 5G era, network slicing has received a great deal of attention as a means
to support a variety of wireless services in a flexible manner. Network slicing is a technique …

Towards ubiquitous AI in 6G with federated learning

Y Xiao, G Shi, M Krunz - arXiv preprint arXiv:2004.13563, 2020 - arxiv.org
With 5G cellular systems being actively deployed worldwide, the research community has
started to explore novel technological advances for the subsequent generation, ie, 6G. It is …

[HTML][HTML] A survey on applications of reinforcement learning in flying ad-hoc networks

S Rezwan, W Choi - Electronics, 2021 - mdpi.com
Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc
networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks …

A network-assisted user-centric WiFi-offloading model for maximizing per-user throughput in a heterogeneous network

BH Jung, NO Song, DK Sung - IEEE Transactions on Vehicular …, 2013 - ieeexplore.ieee.org
In this paper, we propose a novel network-assisted user-centric WiFi-offloading model for
maximizing per-user throughput in a heterogeneous network. In the proposed WiFi …

Learning-aided network association for hybrid indoor LiFi-WiFi systems

J Wang, C Jiang, H Zhang, X Zhang… - IEEE Transactions …, 2017 - ieeexplore.ieee.org
Given the scarcity of spectral resources in traditional wireless networks, it has become
popular to construct visible light communication (VLC) systems. They exhibit high energy …