RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion
This work addresses the need for better generative models in wireless communications, offering a versatile solution for applications like Wi-Fi sensing and 5G channel estimation, though it is incremental as it adapts existing diffusion models to a new domain.
The paper tackles the problem of generating high-quality, time-series radio frequency (RF) data by proposing RF-Diffusion, a method that adapts diffusion models to the RF domain with a novel Time-Frequency Diffusion theory and Hierarchical Diffusion Transformer, achieving superior performance in synthesizing Wi-Fi and FMCW signals compared to three prevalent generative models.
Along with AIGC shines in CV and NLP, its potential in the wireless domain has also emerged in recent years. Yet, existing RF-oriented generative solutions are ill-suited for generating high-quality, time-series RF data due to limited representation capabilities. In this work, inspired by the stellar achievements of the diffusion model in CV and NLP, we adapt it to the RF domain and propose RF-Diffusion. To accommodate the unique characteristics of RF signals, we first introduce a novel Time-Frequency Diffusion theory to enhance the original diffusion model, enabling it to tap into the information within the time, frequency, and complex-valued domains of RF signals. On this basis, we propose a Hierarchical Diffusion Transformer to translate the theory into a practical generative DNN through elaborated design spanning network architecture, functional block, and complex-valued operator, making RF-Diffusion a versatile solution to generate diverse, high-quality, and time-series RF data. Performance comparison with three prevalent generative models demonstrates the RF-Diffusion's superior performance in synthesizing Wi-Fi and FMCW signals. We also showcase the versatility of RF-Diffusion in boosting Wi-Fi sensing systems and performing channel estimation in 5G networks.