  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.
