GAN-Based Channel Generation and Modeling for 6G Intelligent IIoT Communications

With the development of sixth-generation (6G) wireless communication technology, establishing an accurate and effective channel model has become an indispensable technical foundation for the exploitation of novel systems. However, traditional wireless channel modeling methods display obvious deficiencies in the face of more complex scenarios and massive data requirements in 6G communication. In view of this, a novel generative adversarial network (GAN)-based channel generation and modeling method for 6G intelligent industrial internet of things (IIoT) communications is proposed, which is validated with the measurement data obtaining from multi-frequency and multi-scenes IIoT channels. In this methodological framework, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is employed to make predictions on real measurement data. It enables the reconstruction of lost channel data and data augmentation, which effectively address the problem of shortage of the real measurement data. Using the generated channel data, the birth-death (B-D) process of clusters is tracked and then a cluster-based channel model is constructed for 6G intelligent IIoT communicaitons. The characteristics and the survival distance of clusters with different scenes in different frequency bands are further investigated and analyzed. The relevant research provides an innovative approach for channel generation and modeling of 6G IIoT scenarios.