Semantic communication (SemCom) is expected to be a core paradigm in future communication networks, yielding significant benefits in terms of spectrum resource saving and information interaction efficiency. However, the existing SemCom structure is limited by the lack of context-reasoning ability and background knowledge provisioning, which, therefore, motivates us to seek the potential of incorporating generative artificial intelligence (GAI) technologies with SemCom. Recognizing GAI's powerful capability in automating and creating valuable, diverse, and personalized multimodal content, this article first highlights the principal characteristics of the combination of GAI and SemCom along with their pertinent benefits and challenges. To tackle these challenges, we further propose a novel GAI-integrated SemCom network (GAI-SCN) framework in a cloud-edge-mobile design. Specifically, by employing global and local GAI models, our GAI-SCN enables multimodal semantic content provisioning, semantic-level joint-source-channel coding, and AIGC acquisition to maximize the efficiency and reliability of semantic reasoning and resource utilization. Afterward, we present a detailed implementation workflow of GAI-SCN, followed by corresponding initial simulations for performance evaluation in comparison with two benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of GAI-SCN.

翻译：语义通信（SemCom）有望成为未来通信网络的核心范式，在频谱资源节约与信息交互效率方面带来显著效益。然而，现有语义通信体系受限于上下文推理能力与背景知识供给的不足，这促使我们探索将生成式人工智能（GAI）技术与语义通信融合的潜力。鉴于生成式人工智能在自动化生成及创造具有价值性、多样性与个性化的多模态内容方面的强大能力，本文首先阐述了生成式人工智能与语义通信融合的核心特征及其相关优势与挑战。为应对这些挑战，我们进一步提出一种云-边-端协同设计的新型生成式人工智能融合语义通信网络（GAI-SCN）框架。具体而言，通过部署全局与局部生成式人工智能模型，所提GAI-SCN能够实现多模态语义内容供给、语义级联合信源信道编码以及生成式人工智能内容获取，从而最大化语义推理与资源利用的效率及可靠性。随后，我们详细阐述了GAI-SCN的实施流程，并通过与两种基准方案的对比仿真实验进行了初步性能评估。最后，我们探讨了若干开放性问题并提出了可行的解决方案，以充分释放GAI-SCN的潜力。