  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.
