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Rethinking Multi-User Semantic Communications with Deep Generative Models

CoRR(2024)

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Abstract
In recent years, novel communication strategies have emerged to face thechallenges that the increased number of connected devices and the higherquality of transmitted information are posing. Among them, semanticcommunication obtained promising results especially when combined withstate-of-the-art deep generative models, such as large language or diffusionmodels, able to regenerate content from extremely compressed semanticinformation. However, most of these approaches focus on single-user scenariosprocessing the received content at the receiver on top of conventionalcommunication systems. In this paper, we propose to go beyond these methods bydeveloping a novel generative semantic communication framework tailored formulti-user scenarios. This system assigns the channel to users knowing that thelost information can be filled in with a diffusion model at the receivers.Under this innovative perspective, OFDMA systems should not aim to transmit thelargest part of information, but solely the bits necessary to the generativemodel to semantically regenerate the missing ones. The thorough experimentalevaluation shows the capabilities of the novel diffusion model and theeffectiveness of the proposed framework, leading towards a GenAI-based nextgeneration of communications.
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