Conditional Generative Adversarial Networks for Precise Characterization of 6G RF Nonlinear Devices

In 6G wireless communication systems, the hardware performance of RF devices is very important, but accurate modeling of their nonlinear characteristics is often limited by cumbersome experimental procedures and high-cost hardware, which hinders the progress of related research. To solve this problem, this paper proposes a novel modeling method combining generative adversarial network and conditional classifier technology, aiming to reduce the complexity and cost of collecting experimental samples. The generative adversarial network is used to generate nonlinear response data for RF nonlinear devices, while the conditional classifier focuses on feature extraction and model structure optimization. Experimental results show that the proposed method can effectively model 6G RF nonlinear devices and significantly reduce the experimental complexity and research cost.