Generative AI for Physical-Layer Authentication
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912904914141184 |
|---|---|
| author | Meng, Rui Cheng, Xiqi Gao, Song Xu, Xiaodong Dong, Chen Nan, Guoshun Tao, Xiaofeng Zhang, Ping Quek, Tony Q. S. |
| author_facet | Meng, Rui Cheng, Xiqi Gao, Song Xu, Xiaodong Dong, Chen Nan, Guoshun Tao, Xiaofeng Zhang, Ping Quek, Tony Q. S. |
| contents | Recently, Artificial Intelligence (AI)-driven Physical-Layer Authentication (PLA), which focuses on achieving endogenous security and intelligent identity authentication, has attracted considerable interest. When compared with Discriminative AI (DAI), Generative AI (GAI) offers several advantages, such as fingerprint augmentation, reconstruction, and denoising. Inspired by these innovations, this paper provides a systematic exploration of GAI's integration with PLA. We commence with a concise review of identity authentication techniques and GAI models. Then, we contrast the limitations of DAI with the potential of GAI in addressing PLA challenges. Specifically, we introduce a structured taxonomy for GAI-enhanced PLA methodologies, encompassing three key stages: fingerprint collection, model training, and performance optimization within the PLA pipeline. Furthermore, we propose a novel PLA framework based on GAI and channel extrapolation for dynamic environments. To demonstrate GAI's efficacy in enhancing PLA robustness, we implement a case study using the Generative Diffusion Model (GDM). Finally, we outline potential future research directions for GAI-based PLA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18175 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI for Physical-Layer Authentication Meng, Rui Cheng, Xiqi Gao, Song Xu, Xiaodong Dong, Chen Nan, Guoshun Tao, Xiaofeng Zhang, Ping Quek, Tony Q. S. Signal Processing Recently, Artificial Intelligence (AI)-driven Physical-Layer Authentication (PLA), which focuses on achieving endogenous security and intelligent identity authentication, has attracted considerable interest. When compared with Discriminative AI (DAI), Generative AI (GAI) offers several advantages, such as fingerprint augmentation, reconstruction, and denoising. Inspired by these innovations, this paper provides a systematic exploration of GAI's integration with PLA. We commence with a concise review of identity authentication techniques and GAI models. Then, we contrast the limitations of DAI with the potential of GAI in addressing PLA challenges. Specifically, we introduce a structured taxonomy for GAI-enhanced PLA methodologies, encompassing three key stages: fingerprint collection, model training, and performance optimization within the PLA pipeline. Furthermore, we propose a novel PLA framework based on GAI and channel extrapolation for dynamic environments. To demonstrate GAI's efficacy in enhancing PLA robustness, we implement a case study using the Generative Diffusion Model (GDM). Finally, we outline potential future research directions for GAI-based PLA. |
| title | Generative AI for Physical-Layer Authentication |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.18175 |