Abstract:  The performance of deep learning (DL) empowered wireless communications, networking, and sensing depends on the availability of sufficient high-quality radio frequency (RF) data, which is more difficult and expensive to collect than other types. To overcome this obstacle, we propose several AIGC approaches to generate synthetic RF data labeled with specified human activities for multiple wireless sensing platforms, such as WiFi, RFID, mmWave radar, including a conditional Recurrent Generative Adversarial Network (R-GAN) approach and diffusion model based approaches. The high quality of the generated RF data is validated by metrics of Structural Similarity Index (SSIM) and Frechet Inception Distance (FID), as well as representative downstream tasks of human activity recognition (HAR), where the model trained with sufficient synthesized data outperforms the model trained by real data.