Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks

TL;DR

The paper addresses semantic communication with deep generative models, shifting from exact bit recovery to regenerating content semantically aligned with the transmitted meaning, potentially reducing data traffic. It provides a unified perspective across AutoEncoder-based and Diffusion-model approaches and analyzes receiver adaptation under fading channels via joint source-channel coding with a latent prompt set $Z = {z_1, ..., z_L}$ and distortion $D_l = D(x, x_hat(Z_l))$. It further discusses multimodal adaptation and integration with Large Language Models to exploit cross-modal and linguistic capabilities. The work highlights challenges and opportunities for tailoring generative models to communication systems, emphasizing potential practical impact and the need for tailored model design and evaluation frameworks.

Abstract

While deep generative models are showing exciting abilities in computer vision and natural language processing, their adoption in communication frameworks is still far underestimated. These methods are demonstrated to evolve solutions to classic communication problems such as denoising, restoration, or compression. Nevertheless, generative models can unveil their real potential in semantic communication frameworks, in which the receiver is not asked to recover the sequence of bits used to encode the transmitted (semantic) message, but only to regenerate content that is semantically consistent with the transmitted message. Disclosing generative models capabilities in semantic communication paves the way for a paradigm shift with respect to conventional communication systems, which has great potential to reduce the amount of data traffic and offers a revolutionary versatility to novel tasks and applications that were not even conceivable a few years ago. In this paper, we present a unified perspective of deep generative models in semantic communication and we unveil their revolutionary role in future communication frameworks, enabling emerging applications and tasks. Finally, we analyze the challenges and opportunities to face to develop generative models specifically tailored for communication systems.