In this article, 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 predict the detailed variations of the received power as well as the detailed makeup of the signal rays (i.e., amplitude, angle-of-arrival (AoA), and phase of the incoming paths). More specifically, we show how an enclosure-based robotic route design ensures that the received 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 extend them to predict the detailed ray makeup at unvisited locations in the area. We 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.