Generative AI Based Data Augmentation for Integrated Sensing and Communications Networks
Jiacheng Wang, Changyuan Zhao, Ruichen Zhang, Yinqiu Liu, Geng Sun, Nan Ma, Dusit Tao Niyato · 2025
Integrated sensing and communication (ISAC) is emerging as a crucial technology for 6G networks, with channel state information (CSI) based ISAC playing a vital role. These systems utilize various AI models to process and analyze the CSI extracted from wireless communication signals, thereby enabling monitoring of physical spaces and human activities. However, due to the costs and privacy issues, collecting sufficient training CSI data is challenging. In response, this paper proposes a data augmentation system based on the diffusion model. Specifically, we first use the limited samples collected from real-world ISAC scenarios to train a conditional diffusion model, which then generates new samples to enhance sample quantity. Subsequently, we train another diffusion model with noise-free data to reduce noise in these generated samples, thereby further enhancing the sample quality. The evaluation based on the real-world CSI data validates that our approach can effectively enhance the data from both quantity and quality perspectives, thereby supporting the model training in ISAC networks.