  The upper mid-band (FR3) has been recently attracting interest for new
generation of mobile networks, as it provides a promising balance between
spectrum availability and coverage, which are inherent limitations of the sub
6GHz and millimeter wave bands, respectively. In order to efficiently design
and optimize the network, channel modeling plays a key role since FR3 systems
are expected to operate at multiple frequency bands. Data-driven methods,
especially generative adversarial networks (GANs), can capture the intricate
relationships among data samples, and provide an appropriate tool for FR3
channel modeling. In this work, we present the architecture, link state model,
and path generative network of GAN-based FR3 channel modeling. The comparison
of our model greatly matches the ray-tracing simulated data.
