Path-Based Statistical Modeling of Multipath Components in Propagation Channels for Wireless Communications in Unmanned Aviation

D. Mielke, Dennis Becker, M. Walter
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Abstract

Unmanned aviation (UA), including both small drones in urban airspace, as well as larger unmanned airplanes, is one of the most popular topics in aviation these days. One of the key enablers for unmanned aviation is a secure and robust communication link between the air vehicle and the instance controlling and/or monitoring the air vehicle, e.g. a remote pilot. Naturally, parts of this communication link are wireless; here, we assume a terrestrial link between the air vehicle’s radio and the ground stations and a vehicle-to-vehicle communication link between drones.All wireless communication is subject to certain channel effects, e. g. multipath propagation, that usually degrade the radio signal during transmission. A good understanding of these channel effects is of high importance during the development of new wireless waveforms. A common approach to gain knowledge on the characteristics of a wireless channel is to perform channel measurements: The DLR performed several channel measurement campaigns involving smaller drones and a jet aircraft in the recent years. The collected data contain information on the channel characteristics during the respective scenarios. In this paper, we focus on the detection, path-based tracking, and modeling of multipath components and their evolution over time. First, we present our processing chain for the detection and tracking of multipath components and apply it to the collected measurement data. We then introduce a compact representation of the evolution of the detected multipath components over time. The statistical properties of this representation are then used to fit a kernel that is used to generate artificial multipath-components and their evolution. We finally evaluate our approach by comparing the delay spread and the K-factor of the measurement data with the corresponding properties of the data generated by our model.
基于路径的无人机无线通信传播信道多径分量统计建模
无人航空(UA)是当今航空领域最热门的话题之一,既包括城市空域的小型无人机,也包括大型无人飞机。无人驾驶航空的关键促成因素之一是飞行器与控制和/或监视飞行器的实例(例如远程飞行员)之间安全可靠的通信链路。当然,这种通信链路的一部分是无线的;在这里,我们假设飞行器的无线电和地面站之间有地面链路,无人机之间有车对车通信链路。所有的无线通信都受到某些信道效应的影响,例如多径传播,这通常会使无线电信号在传输过程中降级。在开发新的无线波形时,对这些信道效应的充分理解是非常重要的。了解无线信道特性的一种常用方法是进行信道测量:近年来,DLR进行了几次涉及小型无人机和喷气式飞机的信道测量活动。所收集的数据包含在各自场景中的通道特征信息。在本文中,我们关注多路径组件的检测、基于路径的跟踪和建模及其随时间的演变。首先,我们提出了多径分量检测和跟踪的处理链,并将其应用于采集到的测量数据。然后,我们介绍了检测到的多路径组件随时间演变的紧凑表示。然后使用该表示的统计属性来拟合用于生成人工多路径组件及其演变的内核。最后,我们通过比较测量数据的延迟扩展和k因子与我们模型生成的数据的相应属性来评估我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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