Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems

• URL: http://arxiv.org/abs/2502.17517v1
• Date: Sun, 23 Feb 2025 02:57:06 GMT
• Title: Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems
• Authors: Li Dong, Feibo Jiang, Yubo Peng,
• Abstract summary: We present an Attention-based UAV Trajectory Optimization framework based on the graph transformer.<n>In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs.<n>TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network.
• Score: 19.680892841701674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. To address these issues, we present an Attention-based UAV Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (ATOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UAVs. TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UAV trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework.

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