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arXiv 2310.04547eess.SP
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基于深度学习的无人机集群主动空间信道增益预测

Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles

Enes Krijestorac, Danijela Cabric

AI总结:

本文针对空间无线信道增益预测问题,提出两种基于无人机集群的主动预测方法,通过仿真验证了主动预测、3D地图结合深度学习及多机协同路径规划的优势。

摘要:

空间范围内的无线信道增益(CG)预测是解决诸多重要无线网络设计问题的必要工具。本文提出了利用环境特定特征(即建筑物地图与CG测量值)实现高预测精度的预测方法。我们假设测量值由协同工作的无人机(UAV)集群采集。本文提出了全新的主动预测方法,既包含用于最优测量采集的无人机路径规划方法,也包含基于采集到的测量值进行空间CG预测的方法。我们提出了两种分别基于深度学习(DL)和克里金(Kriging)插值的主动预测方法。第一种方法不依赖发射机位置,利用3D地图弥补发射机位置未知的不足。我们采用深度学习将3D地图融入预测过程,并使用强化学习基于深度学习预测结果为无人机规划最优路径。第二种主动预测方法基于克里金插值,该方法需要已知发射机位置且无法利用3D地图。我们在基于射线追踪的信道模拟器中对两种所提方法进行了训练与评估。通过仿真实验,我们证明了主动预测相较于基于随机采集的信道增益测量值的预测具有重要优势。此外,我们表明使用深度学习和3D地图,即使在未知发射机位置的情况下也能实现高预测精度。我们还证明,与无人机以贪婪方式独立采集测量值相比,协同路径规划对于多无人机主动预测具有重要意义。

英文摘要:

Prediction of wireless channel gain (CG) across space is a necessary tool for many important wireless network design problems. In this paper, we develop prediction methods that use environment-specific features, namely building maps and CG measurements, to achieve high prediction accuracy. We assume that measurements are collected using a swarm of coordinated unmanned aerial vehicles (UAVs). We develop novel active prediction approaches which consist of both methods for UAV path planning for optimal measurement collection and methods for prediction of CG across space based on the collected measurements. We propose two active prediction approaches based on deep learning (DL) and Kriging interpolation. The first approach does not rely on the location of the transmitter and utilizes 3D maps to compensate for the lack of it. We utilize DL to incorporate 3D maps into prediction and reinforcement learning for optimal path planning for the UAVs based on DL prediction. The second active prediction approach is based on Kriging interpolation, which requires known transmitter location and cannot utilize 3D maps. We train and evaluate the two proposed approaches in a ray-tracing-based channel simulator. Using simulations, we demonstrate the importance of active prediction compared to prediction based on randomly collected measurements of channel gain. Furthermore, we show that using DL and 3D maps, we can achieve high prediction accuracy even without knowing the transmitter location. We also demonstrate the importance of coordinated path planning for active prediction when using multiple UAVs compared to UAVs collecting measurements independently in a greedy manner.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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