Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908482683273216 |
|---|---|
| author | Ye, Peigen Sun, Yaping Yao, Shumin Chen, Hao Xu, Xiaodong Cui, Shuguang |
| author_facet | Ye, Peigen Sun, Yaping Yao, Shumin Chen, Hao Xu, Xiaodong Cui, Shuguang |
| contents | Codebook-based generative semantic communication attracts increasing attention, since only indices are required to be transmitted when the codebook is shared between transmitter and receiver. However, due to the fact that the semantic relations among code vectors are not necessarily related to the distance of the corresponding code indices, the performance of the codebook-enabled semantic communication system is susceptible to the channel noise. Thus, how to improve the system robustness against the noise requires careful design. This paper proposes a robust codebook-assisted image semantic communication system, where semantic codec and codebook are first jointly constructed, and then vector-to-index transformer is designed guided by the codebook to eliminate the effects of channel noise, and achieve image generation. Thanks to the assistance of the high-quality codebook to the Transformer, the generated images at the receiver outperform those of the compared methods in terms of visual perception. In the end, numerical results and generated images demonstrate the advantages of the generative semantic communication method over JPEG+LDPC and traditional joint source channel coding (JSCC) methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer Ye, Peigen Sun, Yaping Yao, Shumin Chen, Hao Xu, Xiaodong Cui, Shuguang Information Theory Artificial Intelligence Computer Vision and Pattern Recognition Codebook-based generative semantic communication attracts increasing attention, since only indices are required to be transmitted when the codebook is shared between transmitter and receiver. However, due to the fact that the semantic relations among code vectors are not necessarily related to the distance of the corresponding code indices, the performance of the codebook-enabled semantic communication system is susceptible to the channel noise. Thus, how to improve the system robustness against the noise requires careful design. This paper proposes a robust codebook-assisted image semantic communication system, where semantic codec and codebook are first jointly constructed, and then vector-to-index transformer is designed guided by the codebook to eliminate the effects of channel noise, and achieve image generation. Thanks to the assistance of the high-quality codebook to the Transformer, the generated images at the receiver outperform those of the compared methods in terms of visual perception. In the end, numerical results and generated images demonstrate the advantages of the generative semantic communication method over JPEG+LDPC and traditional joint source channel coding (JSCC) methods. |
| title | Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer |
| topic | Information Theory Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.16868 |