Digital Twin Channel Modeling With Generative Adversarial Networks and Transfer Learning

Digital twin (DT) is widely accepted as a promising technology for designing next-generation wireless communication that allows interplaying between physical and digital domains. DT channel modeling is the foundation and prerequisite for building DT communication systems. This paper develops a new method for modeling DT channels using generative adversarial networks (GANs) and transfer learning. First, we integrate an external channel emulator into the simulation platform to gather extensive channel data from various propagation scenarios, which allows us to create a comprehensive dataset. Then, the GAN is applied to model twin channels, and a transfer learning approach is designed to improve training efficiency and enhance the model’s generalization ability across different channel scenarios. Next, the effectiveness of our channel modeling method is validated in terms of two performance metrics: channel power delay profile and frequency correlation function. We perform extensive experiments to compare the performance of models with and without transfer learning, demonstrating that transfer learning can significantly accelerate the model’s convergence and enhance the model’s generalization ability.