  Modern cellular systems rely increasingly on simultaneous communication in
multiple discontinuous bands for macro-diversity and increased bandwidth.
Multi-frequency communication is particularly crucial in the millimeter wave
(mmWave) and Terahertz (THz) frequencies, as these bands are often coupled with
lower frequencies for robustness. Evaluation of these systems requires
statistical models that can capture the joint distribution of the channel paths
across multiple frequencies. This paper presents a general neural network based
methodology for training multi-frequency double directional statistical channel
models. In the proposed approach, each is described as a multi-clustered set,
and a generative adversarial network (GAN) is trained to generate random
multi-cluster profiles where the generated cluster data includes the angles and
delay of the clusters along with the vectors of random received powers,
angular, and delay spread at different frequencies. The model can be readily
applied for multi-frequency link or network layer simulation. The methodology
is demonstrated on modeling urban micro-cellular links at 28 and 140 GHz
trained from extensive ray tracing data. The methodology makes minimal
statistical assumptions and experiments show the model can capture interesting
statistical relationships between frequencies.
