论文标题
无线通道预测和完整射线化妆估算的基础
A Foundation for Wireless Channel Prediction and Full Ray Makeup Estimation Using an Unmanned Vehicle
论文作者
论文摘要
在本文中，我们考虑了无线通道预测的问题，我们有兴趣根据该地区无人车辆收集的少量先前接收的电力测量值来预测未访问的位置的通道质量。我们为通道预测提出了一个新的框架，该框架不仅可以预测接收到的功率的详细变化，还可以预测无线射线的详细构成（即振幅，到达角度和所有传入路径的相位）。更具体地说，我们展示了基于机箱的机器人路由设计如何确保可以利用先前测量位置接收的功率测量值来完全预测未访问的位置的详细射线参数。然后，我们展示如何在先前的测量路线上首先估计详细的射线参数，然后完全扩展它们以预测工作空间中未访问的位置的详细射线化妆。我们通过在三个不同领域的广泛的现实世界实验来实验验证我们提出的框架，并表明我们的方法可以准确预测接收到的通道功率和在一个地区未访问的位置的射线的详细构成，从而超过了无线通道预测中最先进的射线。
In this paper, we consider the problem of wireless channel prediction, where we are interested in predicting the channel quality at unvisited locations in an area of interest, based on a small number of prior received power measurements collected by an unmanned vehicle in the area. We propose a new framework for channel prediction that can not only predict the detailed variations of the received power, but can also predict the detailed makeup of the wireless rays (i.e., amplitude, angle-of-arrival, and phase of all the incoming paths). More specifically, we show how an enclosure-based robotic route design ensures that the received power measurements at the prior measurement locations can be utilized to fully predict detailed ray parameters at unvisited locations. We then show how to first estimate the detailed ray parameters at the prior measurement route and then fully extend them to predict the detailed ray makeup at unvisited locations in the workspace. We experimentally validate our proposed framework through extensive real-world experiments in three different areas, and show that our approach can accurately predict the received channel power and the detailed makeup of the rays at unvisited locations in an area, considerably outperforming the state-of-the-art in wireless channel prediction.
