[HTML][HTML] Multi-agent deep reinforcement learning for user association and resource allocation in integrated terrestrial and non-terrestrial networks

DJ Birabwa, D Ramotsoela, N Ventura - Computer Networks, 2023 - Elsevier
Integrating the terrestrial network with non-terrestrial networks to provide radio access as
anticipated in the beyond 5G networks calls for efficient user association and resource …

Deep multi-agent reinforcement learning for resource allocation in D2D communication underlaying cellular networks

X Zhang, Z Lin, B Ding, B Gu… - 2020 21st Asia-Pacific …, 2020 - ieeexplore.ieee.org
Device-to-device communications underlaying cellular networks have been recognized as
one of the key technologies for the fifth generation (5G) cellular system to improve the …

Deep learning for wireless networked systems: A joint estimation-control-scheduling approach

Z Zhao, W Liu, DE Quevedo, Y Li… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Wireless-networked control system (WNCS) connecting sensors, controllers, and actuators
via wireless communications is a key enabling technology for highly scalable and low-cost …

Deep -Learning-Based Node Positioning for Throughput-Optimal Communications in Dynamic UAV Swarm Network

AM Koushik, F Hu, S Kumar - IEEE Transactions on Cognitive …, 2019 - ieeexplore.ieee.org
In this paper, we study the communication-oriented unmanned air vehicle (UAV) placement
issue in a typical manned-and-unmanned (MUM) airborne network. The MUM network …

Joint resource allocation and computation offloading in mobile edge computing for SDN based wireless networks

N Kiran, C Pan, S Wang, C Yin - Journal of Communications …, 2019 - ieeexplore.ieee.org
The rapid growth of the internet usage and the distributed computing resources of edge
devices create a necessity to have a reasonable controller to ensure efficient utilization of …

DRAG: Deep reinforcement learning based base station activation in heterogeneous networks

J Ye, YJA Zhang - IEEE Transactions on Mobile Computing, 2019 - ieeexplore.ieee.org
Heterogeneous Network (HetNet), where Small cell Base Stations (SBSs) are densely
deployed to offload traffic from macro Base Stations (BSs), is identified as a key solution to …

Learning to branch: Accelerating resource allocation in wireless networks

M Lee, G Yu, GY Li - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
Resource allocation in wireless networks, such as device-to-device (D2D) communications,
is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are …

Dynamic power allocation in IIoT based on multi-agent deep reinforcement learning

F Li, Z Liu, X Zhang, Y Yang - Neurocomputing, 2022 - Elsevier
With the rapidly growing fifth generation (5G) wireless data traffic, the cellular network has
gradually become an important mode for the Industrial Internet of Things (IIoT). To give full …

Transfer learning for autonomous cell activation based on relational reinforcement learning with adaptive reward

G Sun, D Ayepah-Mensah, R Xu… - IEEE Systems …, 2021 - ieeexplore.ieee.org
With the increasing threat of global warming due to high energy consumption of wireless
network infrastructure, cell activation complements the capabilities of next-generation …

Multi-agent reinforcement-learning-based time-slotted channel hopping medium access control scheduling scheme

H Park, H Kim, ST Kim, P Mah - IEEE Access, 2020 - ieeexplore.ieee.org
Time-slotted channel hopping (TSCH) is a medium access control technology that realizes
collision-free wireless network communication by coordinating the media access time and …