  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.
