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Maximizing User Connectivity in AI-Enabled Multi-UAV Networks: A Distributed Strategy Generalized to Arbitrary User Distributions

Autor: Li, Bowei, Xu, Yang, Zhang, Ran, Jiang, Xie, Wang, Miao

Deep reinforcement learning (DRL) has been extensively applied to Multi-Unmanned Aerial Vehicle (UAV) network (MUN) to effectively enable real-time adaptation to complex, time-varying environments. Nevertheless, most of the existing works assume a st

Externí odkaz: http://arxiv.org/abs/2411.05205

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Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew

Autor: Zhang, Ran, Li, Bowei, Zhang, Liyuan, Jiang, Xie, Wang, Miao

Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) are a key component in future mobile networking. To handle the dynamic environments in UCNs, reinforcement learning (RL) has been a promising solution attributed to its strong capabili

Externí odkaz: http://arxiv.org/abs/2409.17139

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When Learning Meets Dynamics: Distributed User Connectivity Maximization in UAV-Based Communication Networks

Autor: Li, Bowei, Tripathi, Saugat, Hosain, Salman, Zhang, Ran, Jiang, Xie, Wang, Miao

Distributed management over Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) has attracted increasing research attention. In this work, we study a distributed user connectivity maximization problem in a UCN. The work features a horiz

Externí odkaz: http://arxiv.org/abs/2409.06010

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