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Modern cellular systems rely increasingly on simultaneous communication inmultiple 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 withlower frequencies for robustness. Evaluation of these systems requiresstatistical models that can capture the joint distribution of the channel pathsacross multiple frequencies. This paper presents a general neural network basedmethodology for training multi-frequency double directional statistical channelmodels. In the proposed approach, each is described as a multi-clustered set,and a generative adversarial network (GAN) is trained to generate randommulti-cluster profiles where the generated cluster data includes the angles anddelay of the clusters along with the vectors of random received powers,angular, and delay spread at different frequencies. The model can be readilyapplied for multi-frequency link or network layer simulation. The methodologyis demonstrated on modeling urban micro-cellular links at 28 and 140 GHztrained from extensive ray tracing data. The methodology makes minimalstatistical assumptions and experiments show the model can capture interestingstatistical relationships between frequencies.

Multi-Frequency Channel Modeling for Millimeter Wave and THz Wireless Communication via Generative Adversarial Networks