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Machine Learning for Position Prediction and Determination in Aerial Base Station System

机译：空中基站系统中用于位置预测和确定的机器学习

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摘要

A novel framework for dynamic 3-D deployment of unmanned aerial vehicle (UAV) in the aerial base station system (ABSS) that based on the machine learning algorithms is proposed. In the framework, the UAV is deployed as an aerial base station to serve a group of ground users and is placed based on the prediction of the users' mobility. The joint problem of prediction of users' track and 3-D deployment of the UAV is formulated for maximizing the sum transmit rate. A two-step approach is proposed for predicting the movement of users and for determining the dynamic 3-D placement of the UAV. Firstly, an echo state network (ESN) based prediction algorithm is utilized for predicting the future positions of users based on the real-world datasets collected from Twitter. Secondly, an iterative K-Means based algorithm is proposed for obtaining the optimal placement of UAV at each time slot based on the output of ESN model. Numerical results are illustrated for showing the superiority of the proposed algorithm over the prevalent algorithm on prediction tasks. The accuracy and efficiency of the proposed framework are also investigated. Additionally, compared with static placement of the UAV, the advantage of dynamic 3-D deployment is demonstrated.
机译：提出了一种基于机器学习算法的无人机系统（ABSS）动态3-D部署的新框架。在该框架中，UAV被部署为为一组地面用户提供服务的空中基站，并根据用户移动性的预测进行放置。提出了预测用户航迹和无人机进行3-D部署的联合问题，以最大程度地提高总传输速率。提出了一种分两步的方法来预测用户的运动并确定无人机的动态3-D位置。首先，基于回声状态网络（ESN）的预测算法用于基于从Twitter收集的真实数据集来预测用户的未来位置。其次，基于ESN模型的输出，提出了一种基于迭代K均值的算法，以获取无人机在每个时隙的最优位置。数值结果说明了该算法在预测任务上优于现有算法的优越性。还研究了所提出框架的准确性和效率。另外，与无人机的静态放置相比，动态3D部署的优势得到了证明。

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