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.

翻译：近年来，为应对联网设备数量激增及传输信息质量提升带来的挑战，新型通信策略不断涌现。其中，语义通信取得了显著成果，尤其是与大型语言模型或扩散模型等先进深度生成模型相结合时，能够从极度压缩的语义信息中重建内容。然而，现有方法大多聚焦于单用户场景，在传统通信系统的基础上处理接收端内容。本文提出突破这些方法的局限，开发了一种专为多用户场景定制的新型生成式语义通信框架。该系统在分配信道时，允许用户利用接收端的扩散模型填补丢失信息。基于这一创新视角，OFDMA系统无需传输大部分信息，而仅需传输生成模型语义重建缺失内容所需的必要比特。全面的实验评估展示了新型扩散模型的性能及所提框架的有效性，为基于生成式人工智能的下一代通信技术奠定了基础。