Deep-reinforcement-learning-based proportional fair scheduling control scheme for underlay D2D communication
In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-
enabled devices in different applications starting from e-healthcare, transportation …
enabled devices in different applications starting from e-healthcare, transportation …
A review of machine learning techniques for enhanced energy efficient 5G and 6G communications
TP Fowdur, B Doorgakant - Engineering Applications of Artificial …, 2023 - Elsevier
Cellular technologies have evolved continuously from the 1st to the 5th generation (5G) to
meet the exponentially growing needs of bandwidth, throughput and latency. However, the …
meet the exponentially growing needs of bandwidth, throughput and latency. However, the …
[HTML][HTML] Leveraging machine-learning for D2D communications in 5G/beyond 5G networks
Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G)
and beyond 5G (B5G) networks. Although D2D communication provides several benefits …
and beyond 5G (B5G) networks. Although D2D communication provides several benefits …
A deep reinforcement learning framework for contention-based spectrum sharing
The increasing number of wireless devices operating in unlicensed spectrum motivates the
development of intelligent adaptive approaches to spectrum access. We consider …
development of intelligent adaptive approaches to spectrum access. We consider …
Multi-agent reinforcement learning for dynamic resource management in 6G in-X subnetworks
The 6G network enables a subnetwork-wide evolution, resulting in a “network of
subnetworks”. However, due to the dynamic mobility of wireless subnetworks, the data …
subnetworks”. However, due to the dynamic mobility of wireless subnetworks, the data …
Task allocation on layered multiagent systems: When evolutionary many-objective optimization meets deep Q-learning
This article is concerned with the multitask multiagent allocation problem via many-objective
optimization for multiagent systems (MASs). First, a novel layered MAS model is constructed …
optimization for multiagent systems (MASs). First, a novel layered MAS model is constructed …
[HTML][HTML] Deep reinforcement learning multi-agent system for resource allocation in industrial internet of things
J Rosenberger, M Urlaub, F Rauterberg, T Lutz, A Selig… - Sensors, 2022 - mdpi.com
The high number of devices with limited computational resources as well as limited
communication resources are two characteristics of the Industrial Internet of Things (IIoT) …
communication resources are two characteristics of the Industrial Internet of Things (IIoT) …
Collaborative multiagent reinforcement learning aided resource allocation for uav anti-jamming communication
In this article, we investigate the anti-jamming problem with joint channel and power
allocation for unmanned aerial vehicle (UAV) networks. In particular, we focus on avoiding …
allocation for unmanned aerial vehicle (UAV) networks. In particular, we focus on avoiding …
Blockchain-based resource trading in multi-UAV-assisted industrial IoT networks: A multi-agent DRL approach
With the Industrial Internet of Things (IIoT), mobile devices (MDs) and their demands for low-
latency data communication are increasing. Due to the limited resources of MDs, such as …
latency data communication are increasing. Due to the limited resources of MDs, such as …
[HTML][HTML] Survey of reinforcement-learning-based mac protocols for wireless ad hoc networks with a mac reference model
Z Zheng, S Jiang, R Feng, L Ge, C Gu - Entropy, 2023 - mdpi.com
In this paper, we conduct a survey of the literature about reinforcement learning (RL)-based
medium access control (MAC) protocols. As the scale of the wireless ad hoc network …
medium access control (MAC) protocols. As the scale of the wireless ad hoc network …