Generative Semantic Communication: Architectures, Technologies, and Applications

Engineering ›› DOI: 10.1016/j.eng.2025.07.022

review-article

Jinke Ren ^a^,^b^,^c
, Yaping Sun ^a^,^d^,^*
, Hongyang Du ^e
, Weiwen Yuan ^a^,^b^,^c
, Chongjie Wang ^a^,^b^,^c
, Xianda Wang ^a^,^b^,^c
, Yingbin Zhou ^a^,^b^,^c
, Ziwei Zhu ^a^,^b^,^c
, Fangxin Wang ^a^,^b^,^c
, Shuguang Cui ^a^,^b^,^c^,^*

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Abstract

Semantic communication (SemCom) has emerged as a transformative paradigm for future wireless networks, aiming to improve communication efficiency by transmitting only the semantic meaning (or its encoded version) of the source data rather than the complete set of bits (symbols). However, traditional deep learning-based SemCom systems present challenges such as limited generalization, low robustness, and inadequate reasoning capabilities, primarily due to the inherently discriminative nature of deep neural networks. To address these limitations, generative artificial intelligence (GAI) is seen as a promising solution, offering notable advantages in learning complex data distributions, transforming data between high- and low-dimensional spaces, and generating high-quality content.

Keywords

Semantic communication / Generative artificial intelligence / Large language model / Variational autoencoder / Generative adversarial network / Diffusion model

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Jinke Ren, Yaping Sun, Hongyang Du, Weiwen Yuan, Chongjie Wang, Xianda Wang, Yingbin Zhou, Ziwei Zhu, Fangxin Wang, Shuguang Cui. Generative Semantic Communication: Architectures, Technologies, and Applications. Engineering DOI:10.1016/j.eng.2025.07.022

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