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，新型通信策略不断涌现，以应对连接设备数量增长及传输信息质量提升带来的挑战。其中，语义通信取得了显著成果，尤其是与最先进的深度生成模型（如大型语言模型或扩散模型）相结合时，能够从极度压缩的语义信息中重建内容。然而，大多数方法聚焦于单用户场景，仅能在传统通信系统的基础上由接收端处理接收内容。本文提出超越现有方法，构建了一种针对多用户场景的新型生成式语义通信框架。该框架将信道分配给用户，并依托接收端的扩散模型填补丢失信息。基于这一创新视角，OFDMA系统不应以传输最大信息量为目标，而仅需传输生成模型在语义层面重建缺失内容所需的必要比特。详尽的实验评估验证了新型扩散模型的能力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。