This paper addresses the problem of Age-of-Information (AoI) in UAV-assisted networks. Our objective is to minimize the expected AoI across devices by optimizing UAVs' stopping locations and device selection probabilities. To tackle this problem, we first derive a closed-form expression of the expected AoI that involves the probabilities of selection of devices. Then, we formulate the problem as a non-convex minimization subject to quality of service constraints. Since the problem is challenging to solve, we propose an Ensemble Deep Neural Network (EDNN) based approach which takes advantage of the dual formulation of the studied problem. Specifically, the Deep Neural Networks (DNNs) in the ensemble are trained in an unsupervised manner using the Lagrangian function of the studied problem. Our experiments show that the proposed EDNN method outperforms traditional DNNs in reducing the expected AoI, achieving a remarkable reduction of $29.5\%$.

翻译：本文研究了无人机辅助网络中的信息年龄（AoI）最小化问题。我们的目标是通过优化无人机停留位置和设备选择概率，最小化所有设备的平均期望AoI。为解决该问题，我们首先推导出包含设备选择概率的期望AoI闭合表达式。随后将该问题建模为服从服务质量约束的非凸最小化问题。鉴于问题求解的挑战性，我们提出一种基于集成深度神经网络（EDNN）的方法，该方法利用了所研究问题的对偶形式。具体而言，集成中的深度神经网络（DNN）通过所研究问题的拉格朗日函数以无监督方式进行训练。实验表明，本文提出的EDNN方法在降低期望AoI方面优于传统DNN，实现了29.5%的显著降幅。