  We present NeWRF, a deep learning framework for predicting wireless channels.
Wireless channel prediction is a long-standing problem in the wireless
community and is a key technology for improving the coverage of wireless
network deployments. Today, a wireless deployment is evaluated by a site survey
which is a cumbersome process requiring an experienced engineer to perform
extensive channel measurements. To reduce the cost of site surveys, we develop
NeWRF, which is based on recent advances in Neural Radiance Fields (NeRF).
NeWRF trains a neural network model with a sparse set of channel measurements,
and predicts the wireless channel accurately at any location in the site. We
introduce a series of techniques that integrate wireless propagation properties
into the NeRF framework to account for the fundamental differences between the
behavior of light and wireless signals. We conduct extensive evaluations of our
framework and show that our approach can accurately predict channels at
unvisited locations with significantly lower measurement density than prior
state-of-the-art
