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-assisted 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：语义通信有望成为未来通信网络的核心范式，在频谱资源节省和信息交互效率方面具有显著优势。然而，现有语义通信结构受限于缺乏上下文推理能力和背景知识提供能力，这促使我们探索将生成式人工智能技术与语义通信相结合的潜力。鉴于生成式人工智能在自动生成有价值、多样化且个性化的多模态内容方面具有强大能力，本文首先阐述了生成式人工智能与语义通信融合的主要特征，以及其相关优势与挑战。为解决这些挑战，我们进一步提出了一种新型生成式人工智能辅助语义通信网络框架，采用云-边-端协同设计。具体而言，通过部署全局和局部生成式人工智能模型，我们的生成式人工智能辅助语义通信网络能够实现多模态语义内容提供、语义级联合源信道编码以及人工智能生成内容获取，从而最大化语义推理和资源利用的效率与可靠性。随后，我们展示了生成式人工智能辅助语义通信网络的详细实现流程，并进行了与两个基准方案对比的初始仿真性能评估。最后，我们讨论了若干开放性问题，并提出了可行的解决方案，以充分释放生成式人工智能辅助语义通信网络的潜力。