[2511.08416] Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications

Electrical Engineering and Systems Science > Signal Processing arXiv:2511.08416 (eess) [Submitted on 11 Nov 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications Authors:Hai-Long Qin, Jincheng Dai, Guo Lu, Shuo Shao, Sixian Wang, Tongda Xu, Wenjun Zhang, Ping Zhang, Khaled B. Letaief View a PDF of the paper titled Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications, by Hai-Long Qin and 8 other authors View PDF Abstract:Semantic communications mark a paradigm shift from bit-accurate transmission toward meaning-centric communication, essential as wireless systems approach theoretical capacity limits. The emergence of generative AI has catalyzed generative semantic communications, where receivers reconstruct content from minimal semantic cues by leveraging learned priors. Among generative approaches, diffusion models stand out for their superior generation quality, stable training dynamics, and rigorous theoretical foundations. However, the field currently lacks systematic guidance connecting diffusion techniques to communication system design, forcing researchers to navigate disparate literatures. This article provides the first comprehensive tutorial on diffusion models for generative semantic communications. We present score-based diffusion foundations and systematically review three technical pillars: conditional diffusion for controllable generation, effici...