Diff-GO$^\text{n}$: Enhancing Diffusion Models for Goal-Oriented Communications
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912626661916672 |
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| author | Wanninayaka, Suchinthaka Wijesinghe, Achintha Wang, Weiwei Chao, Yu-Chieh Zhang, Songyang Ding, Zhi |
| author_facet | Wanninayaka, Suchinthaka Wijesinghe, Achintha Wang, Weiwei Chao, Yu-Chieh Zhang, Songyang Ding, Zhi |
| contents | The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GO-COM, this work introduces a novel noise-restricted diffusion-based GO-COM (Diff-GO$^\text{n}$) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing high-quality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GO$^\text{n}$ well-suited for real-time communications and downstream applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06980 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diff-GO$^\text{n}$: Enhancing Diffusion Models for Goal-Oriented Communications Wanninayaka, Suchinthaka Wijesinghe, Achintha Wang, Weiwei Chao, Yu-Chieh Zhang, Songyang Ding, Zhi Image and Video Processing The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GO-COM, this work introduces a novel noise-restricted diffusion-based GO-COM (Diff-GO$^\text{n}$) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing high-quality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GO$^\text{n}$ well-suited for real-time communications and downstream applications. |
| title | Diff-GO$^\text{n}$: Enhancing Diffusion Models for Goal-Oriented Communications |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2412.06980 |