Learnable Residual-Based Latent Denoising in Semantic Communication
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916739104636928 |
|---|---|
| author | Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Zhang, Wenjun Zhang, Ping |
| author_facet | Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Zhang, Wenjun Zhang, Ping |
| contents | A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learnable Residual-Based Latent Denoising in Semantic Communication Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Zhang, Wenjun Zhang, Ping Machine Learning Information Theory A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images. |
| title | Learnable Residual-Based Latent Denoising in Semantic Communication |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2502.07319 |