Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913025474166784 |
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| author | Grassucci, Eleonora Choi, Jinho Park, Jihong Gramaccioni, Riccardo F. Cicchetti, Giordano Comminiello, Danilo |
| author_facet | Grassucci, Eleonora Choi, Jinho Park, Jihong Gramaccioni, Riccardo F. Cicchetti, Giordano Comminiello, Danilo |
| contents | In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information are posing. Among them, semantic communication obtained promising results especially when combined with state-of-the-art deep generative models, such as large language or diffusion models, able to regenerate content from extremely compressed semantic information. However, most of these approaches focus on single-user scenarios processing the received content at the receiver on top of conventional communication systems. In this paper, we propose to go beyond these methods by developing a novel generative semantic communication framework tailored for multi-user scenarios. This system assigns the channel to users knowing that the lost information can be filled in with a diffusion model at the receivers. Under this innovative perspective, OFDMA systems should not aim to transmit the largest part of information, but solely the bits necessary to the generative model to semantically regenerate the missing ones. The thorough experimental evaluation shows the capabilities of the novel diffusion model and the effectiveness of the proposed framework, leading towards a GenAI-based next generation of communications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09866 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling Grassucci, Eleonora Choi, Jinho Park, Jihong Gramaccioni, Riccardo F. Cicchetti, Giordano Comminiello, Danilo Signal Processing Machine Learning In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information are posing. Among them, semantic communication obtained promising results especially when combined with state-of-the-art deep generative models, such as large language or diffusion models, able to regenerate content from extremely compressed semantic information. However, most of these approaches focus on single-user scenarios processing the received content at the receiver on top of conventional communication systems. In this paper, we propose to go beyond these methods by developing a novel generative semantic communication framework tailored for multi-user scenarios. This system assigns the channel to users knowing that the lost information can be filled in with a diffusion model at the receivers. Under this innovative perspective, OFDMA systems should not aim to transmit the largest part of information, but solely the bits necessary to the generative model to semantically regenerate the missing ones. The thorough experimental evaluation shows the capabilities of the novel diffusion model and the effectiveness of the proposed framework, leading towards a GenAI-based next generation of communications. |
| title | Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2405.09866 |