RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913314566569984 |
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| author | Chi, Guoxuan Yang, Zheng Wu, Chenshu Xu, Jingao Gao, Yuchong Liu, Yunhao Han, Tony Xiao |
| author_facet | Chi, Guoxuan Yang, Zheng Wu, Chenshu Xu, Jingao Gao, Yuchong Liu, Yunhao Han, Tony Xiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion Chi, Guoxuan Yang, Zheng Wu, Chenshu Xu, Jingao Gao, Yuchong Liu, Yunhao Han, Tony Xiao Machine Learning Information Theory Signal Processing I.2.0 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. |
| title | RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion |
| topic | Machine Learning Information Theory Signal Processing I.2.0 |
| url | https://arxiv.org/abs/2404.09140 |