Distributed Safe Multi-Agent Reinforcement Learning: Joint Design of THz-enabled UAV Trajectory and Channel Allocation
A Termehchi, A Syed, WS Kennedy… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
6G is anticipated to play a foundational role in realizing various emerging entertainment
applications and critical societal services, such as smart agriculture, public safety, and so on …
applications and critical societal services, such as smart agriculture, public safety, and so on …
Cooperative multi-agent deep reinforcement learning for reliable and energy-efficient mobile access via multi-UAV control
This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based
positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration (ie, UAVs …
positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration (ie, UAVs …
3TO: THz-enabled throughput and trajectory optimization of UAVs in 6G networks by proximal policy optimization deep reinforcement learning
Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore,
this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled …
this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled …
Multi-agent deep reinforcement learning for secure UAV communications
In this paper, we investigate a multi-unmanned aerial vehicle (UAV) cooperation mechanism
for secure communications, where the UAV transmitter moves around to serve the multiple …
for secure communications, where the UAV transmitter moves around to serve the multiple …
Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication
In the multiple unmanned aerial vehicle (UAV)-assisted downlink communication, it is
challenging for UAV base stations (UAV BSs) to realize trajectory design and resource …
challenging for UAV base stations (UAV BSs) to realize trajectory design and resource …
Joint UAV trajectory and communication design with heterogeneous multi-agent reinforcement learning
X Zhou, J Xiong, H Zhao, X Liu, B Ren, X Zhang… - Science China …, 2024 - Springer
Unmanned aerial vehicles (UAVs) are recognized as effective means for delivering
emergency communication services when terrestrial infrastructures are unavailable. This …
emergency communication services when terrestrial infrastructures are unavailable. This …
QoE-driven adaptive deployment strategy of multi-UAV networks based on hybrid deep reinforcement learning
Y Zhou, X Ma, S Hu, D Zhou… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) serve as aerial base stations to provide controlled
wireless connections for ground users. Due to their constraints on both mobility and energy …
wireless connections for ground users. Due to their constraints on both mobility and energy …
5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul
Fast and reliable wireless communication has become a critical demand in human life. In the
case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing …
case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing …
[HTML][HTML] Coordinated multi-agent deep reinforcement learning for energy-aware UAV-based big-data platforms
This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL)
algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to …
algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to …
Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks
Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-
UAV networks. However, the training of DRL relies on the frequent interactions between the …
UAV networks. However, the training of DRL relies on the frequent interactions between the …