Multi-agent reinforcement learning based resource management in MEC-and UAV-assisted vehicular networks
In this paper, we investigate multi-dimensional resource management for unmanned aerial
vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource …
vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource …
User association in a VHetNet with delayed CSI: A deep reinforcement learning approach
H Khoshkbari, S Sharifi… - IEEE Communications …, 2023 - ieeexplore.ieee.org
Non-terrestrial base stations (NTBSs) must be employed for next-generation wireless
networks to provide users with ubiquitous connectivity and a higher data rate. In vertical …
networks to provide users with ubiquitous connectivity and a higher data rate. In vertical …
Dynamic power allocation in cellular network based on multi-agent double deep reinforcement learning
Y Yang, F Li, X Zhang, Z Liu, KY Chan - Computer Networks, 2022 - Elsevier
With the massively growing wireless data traffic, the dense cellular network has become a
significant mode for the fifth generation (5G) network. To fully utilize the benefit of the cellular …
significant mode for the fifth generation (5G) network. To fully utilize the benefit of the cellular …
AIF: An artificial intelligence framework for smart wireless network management
To solve the policy optimizing problem in many scenarios of smart wireless network
management using a single universal algorithm, this letter proposes a universal learning …
management using a single universal algorithm, this letter proposes a universal learning …
Semantic-aware collaborative deep reinforcement learning over wireless cellular networks
Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can
coordinate over a wireless network is a promising approach to enable future intelligent and …
coordinate over a wireless network is a promising approach to enable future intelligent and …
On-demand channel bonding in heterogeneous WLANs: A multi-agent deep reinforcement learning approach
In order to meet the ever-increasing traffic demand of Wireless Local Area Networks
(WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel …
(WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel …
Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems
The design of Wireless Networked Control System (WNCS) requires addressing critical
interactions between control and communication systems with minimal complexity and …
interactions between control and communication systems with minimal complexity and …
Dynamic adaptation of contention window boundaries using deep Q networks in UAV swarms
In flying ad hoc networks (FANET), medium access layer (MAC) protocols play an essential
role in ensuring better network performance. The effective utilization of network resources …
role in ensuring better network performance. The effective utilization of network resources …
QoS-aware UAV-BS deployment optimization based on reinforcement learning
H Lee, C Eom, C Lee - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
We propose an unmanned aerial vehicle mounted base station (UAV-BS) deployment
optimization scheme using reinforcement learning (RL). We formulate the objective function …
optimization scheme using reinforcement learning (RL). We formulate the objective function …
BE-DCF: Barring-enhanced distributed coordination function for machine type communications in IEEE 802.11 networks
In contention-based wireless networks, the efficiency of channel access mechanism is
greatly affected by the contention level. The basic channel access mechanism, ie distributed …
greatly affected by the contention level. The basic channel access mechanism, ie distributed …