  Due to the high complexity of geometry-deterministic wireless channel
modeling and the difficulty in its implementation, geometry-based stochastic
channel modeling (GBSM) approaches have been used to evaluate wireless systems.
This paper introduces a new method to model any GBSM by training a generative
neural network using images formed by channel parameters. In this work, we
obtain channel parameters from the ray-tracing simulation in a specific area
and process them in the form of images to train any generative model. Through a
case study, we confirm that the use of channel images completes the training of
the generative model within 10 epochs. In addition, we show that the trained
generative model based on channel images faithfully represents the
distributions of the original data under the assigned conditions. Therefore, we
argue that the proposed data-to-image methods will facilitate the modeling of
GBSM using any generative neural network under general wireless conditions.
