  The radio wave propagation channel is central to the performance of wireless
communication systems. In this paper, we introduce a novel machine
learning-empowered methodology for wireless channel modeling. The key
ingredients include a point-cloud-based neural network and a Spherical
Harmonics encoder with light probes. Our approach offers several significant
advantages, including the flexibility to adjust antenna radiation patterns and
transmitter/receiver locations, the capability to predict radio power maps, and
the scalability of large-scale wireless scenes. As a result, it lays the
groundwork for an end-to-end pipeline for network planning and deployment
optimization. The proposed work is validated in various outdoor and indoor
radio environments.
