Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet

F Tang, Y Zhou, N Kato - IEEE Journal on selected areas in …, 2020 - ieeexplore.ieee.org
Recently, the 5G is widely deployed for supporting communications of high mobility nodes
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …

Machine learning meets communication networks: Current trends and future challenges

I Ahmad, S Shahabuddin, H Malik, E Harjula… - IEEE …, 2020 - ieeexplore.ieee.org
The growing network density and unprecedented increase in network traffic, caused by the
massively expanding number of connected devices and online services, require intelligent …

A distributed mobile fog computing scheme for mobile delay-sensitive applications in SDN-enabled vehicular networks

C Lin, G Han, X Qi, M Guizani… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
With the rapid development of intelligent transportation systems, enormous amounts of delay-
sensitive vehicular services have been emerging and challenge both the architectures and …

Spatial anti-jamming scheme for internet of satellites based on the deep reinforcement learning and Stackelberg game

C Han, L Huo, X Tong, H Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The anti-jamming communication of the heterogeneous Internet of Satellites (IoS) has drawn
increasing attentions due to the smart jamming and high dynamics. This paper investigates …

Distributed learning for automatic modulation classification in edge devices

Y Wang, L Guo, Y Zhao, J Yang… - IEEE Wireless …, 2020 - ieeexplore.ieee.org
Automatic modulation classification (AMC) is a typical technology for identifying different
modulation types, which has been widely applied into various scenarios. Recently, deep …

Enabling multiple power beacons for uplink of NOMA-enabled mobile edge computing in wirelessly powered IoT

DT Do, MS Van Nguyen, TN Nguyen, X Li… - IEEE Access, 2020 - ieeexplore.ieee.org
As a promising technique, power beacon provides ability of wireless energy transfer to
relaying devices in Internet of Things (IoT) to serve far devices with respect to low latency …

Downlink transmit power control in ultra-dense UAV network based on mean field game and deep reinforcement learning

L Li, Q Cheng, K Xue, C Yang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
As an emerging technology in 5G, ultra-dense unmanned aerial vehicles (UAVs) network
can significantly improve the system capacity and networks coverage. However, it is still a …

SDN-enabled adaptive and reliable communication in IoT-fog environment using machine learning and multiobjective optimization

A Akbar, M Ibrar, MA Jan, AK Bashir… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
The Internet-of-Things (IoT) devices, backed by resourceful fog computing, are capable of
meeting the requirements of computationally-intensive tasks. However, many existing IoT …

Joint unmanned aerial vehicle (UAV) deployment and power control for Internet of Things networks

S Fu, Y Tang, N Zhang, L Zhao, S Wu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this paper, we study unmanned aerial vehicle (UAV) aided internet of things (IoT)
networks, where UAVs facilitate data transmission of IoT devices. We focus on uplink …

Reinforcement learning enabled dynamic resource allocation in the internet of vehicles

H Liang, X Zhang, X Hong, Z Zhang… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
As an important application scenario of the industrial Internet of things, the Internet of
Vehicles can significantly improve road safety, improve traffic management efficiency, and …