This paper delves into the applications of generative artificial intelligence (GAI) in semantic communication (SemCom) and presents a thorough study. Three popular SemCom systems enabled by classical GAI models are first introduced, including variational autoencoders, generative adversarial networks, and diffusion models. For each system, the fundamental concept of the GAI model, the corresponding SemCom architecture, and the associated literature review of recent efforts are elucidated. Then, a novel generative SemCom system is proposed by incorporating the cutting-edge GAI technology-large language models (LLMs). This system features two LLM-based AI agents at both the transmitter and receiver, serving as "brains" to enable powerful information understanding and content regeneration capabilities, respectively. This innovative design allows the receiver to directly generate the desired content, instead of recovering the bit stream, based on the coded semantic information conveyed by the transmitter. Therefore, it shifts the communication mindset from "information recovery" to "information regeneration" and thus ushers in a new era of generative SemCom. A case study on point-to-point video retrieval is presented to demonstrate the superiority of the proposed generative SemCom system, showcasing a 99.98% reduction in communication overhead and a 53% improvement in retrieval accuracy compared to the traditional communication system. Furthermore, four typical application scenarios for generative SemCom are delineated, followed by a discussion of three open issues warranting future investigation. In a nutshell, this paper provides a holistic set of guidelines for applying GAI in SemCom, paving the way for the efficient implementation of generative SemCom in future wireless networks.

翻译：本文深入探讨了生成式人工智能在语义通信中的应用，并进行了全面研究。首先介绍了三种由经典生成式人工智能模型实现的流行语义通信系统，包括变分自编码器、生成对抗网络和扩散模型。针对每个系统，阐述了生成式人工智能模型的基本原理、相应的语义通信架构以及相关的最新研究文献综述。随后，通过引入前沿的生成式人工智能技术——大语言模型，提出了一种新颖的生成式语义通信系统。该系统在发送端和接收端分别部署了两个基于大语言模型的人工智能代理，作为"大脑"分别实现强大的信息理解与内容再生能力。这种创新设计使得接收端能够基于发送端传递的编码语义信息直接生成所需内容，而非恢复比特流。因此，它将通信范式从"信息恢复"转变为"信息再生"，从而开创了生成式语义通信的新纪元。通过点对点视频检索的案例研究，展示了所提生成式语义通信系统的优越性：与传统通信系统相比，通信开销降低99.98%，检索准确率提升53%。此外，本文阐述了生成式语义通信的四个典型应用场景，并讨论了三个值得未来研究的开放性问题。总而言之，本文为生成式人工智能在语义通信中的应用提供了一套完整的指导原则，为未来无线网络中高效实现生成式语义通信铺平了道路。