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In this paper, we consider the problem of wireless channel prediction, wherewe are interested in predicting the channel quality at unvisited locations inan area of interest, based on a small number of prior received powermeasurements collected by an unmanned vehicle in the area. We propose a newframework for channel prediction that can not only predict the detailedvariations of the received power, but can also predict the detailed makeup ofthe wireless rays (i.e., amplitude, angle-of-arrival, and phase of all theincoming paths). More specifically, we show how an enclosure-based roboticroute design ensures that the received power measurements at the priormeasurement locations can be utilized to fully predict detailed ray parametersat unvisited locations. We then show how to first estimate the detailed rayparameters at the prior measurement route and then fully extend them to predictthe detailed ray makeup at unvisited locations in the workspace. Weexperimentally validate our proposed framework through extensive real-worldexperiments in three different areas, and show that our approach can accuratelypredict the received channel power and the detailed makeup of the rays atunvisited locations in an area, considerably outperforming the state-of-the-artin wireless channel prediction.

A Foundation for Wireless Channel Prediction and Full Ray Makeup Estimation Using an Unmanned Vehicle