  Prediction of wireless channel gain (CG) across space is a necessary tool for
many important wireless network design problems. In this paper, we develop
prediction methods that use environment-specific features, namely building maps
and CG measurements, to achieve high prediction accuracy. We assume that
measurements are collected using a swarm of coordinated unmanned aerial
vehicles (UAVs). We develop novel active prediction approaches which consist of
both methods for UAV path planning for optimal measurement collection and
methods for prediction of CG across space based on the collected measurements.
We propose two active prediction approaches based on deep learning (DL) and
Kriging interpolation. The first approach does not rely on the location of the
transmitter and utilizes 3D maps to compensate for the lack of it. We utilize
DL to incorporate 3D maps into prediction and reinforcement learning for
optimal path planning for the UAVs based on DL prediction. The second active
prediction approach is based on Kriging interpolation, which requires known
transmitter location and cannot utilize 3D maps. We train and evaluate the two
proposed approaches in a ray-tracing-based channel simulator. Using
simulations, we demonstrate the importance of active prediction compared to
prediction based on randomly collected measurements of channel gain.
Furthermore, we show that using DL and 3D maps, we can achieve high prediction
accuracy even without knowing the transmitter location. We also demonstrate the
importance of coordinated path planning for active prediction when using
multiple UAVs compared to UAVs collecting measurements independently in a
greedy manner.
