Oct 11, 2021 · To meet these requirements, we propose a deep reinforcement learning (DRL) framework, called DISCOUNT (Dispatch of UAVs for Urban VANETs).
To meet these requirements, we propose a deep reinforcement learning (DRL) framework, called DISCOUNT (Dispatch of UAVs for Urban VANETs). Extensive simulations ...
In [11], the authors presented an iterative approach where a minimal number of UAVs are deployed to improve the communication coverage for user equipments (UEs) ...
Ben-Othman, Dispatch of UAVs for Urban Vehicular Networks: A Deep Reinforcement Learning Approach, IEEE Transactions on Vehicular Technology, 2021. O. S. ...
UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight links, and flexible mobility. In this paper, we study a UAV-assisted ...
Dec 21, 2023 · This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically ...
Oct 28, 2024 · This paper studies a fixed-wing unmanned aerial vehicle (UAV) assisted mobile relaying network (FUAVMRN), where a fixed-wing UAV employs an ...
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Jul 25, 2024 · It leveraged Multi-Agent Deep Reinforcement Learning (MADRL) to optimize UAV trajectories, transmit power, and jamming power. The research has ...
A combination of deep reinforcement learning (DRL) and the long-short-term memory (LSTM) network is adopted to accelerate the convergence speed of the ...
This approach was used to test potential re-orchestration scenarios within the vehicular network. By decoupling the functions of vehicular nodes and loading ...