Semantic Communication (SemCom) is envisaged as the next-generation paradigm to address challenges stemming from the conflicts between the increasing volume of transmission data and the scarcity of spectrum resources. However, existing SemCom systems face drawbacks, such as low explainability, modality rigidity, and inadequate reconstruction functionality. Recognizing the transformative capabilities of AI-generated content (AIGC) technologies in content generation, this paper explores a pioneering approach by integrating them into SemCom to address the aforementioned challenges. We employ a three-layer model to illustrate the proposed AIGC-assisted SemCom (AIGC-SCM) architecture, emphasizing its clear deviation from existing SemCom. Grounded in this model, we investigate various AIGC technologies with the potential to augment SemCom’s performance. In alignment with SemCom’s goal of conveying semantic meanings, we also introduce the new evaluation methods for our AIGC-SCM system. Subsequently, we explore communication scenarios where our proposed AIGC-SCM can realize its potential. For practical implementation, we construct a detailed integration workflow and conduct a case study in a virtual reality image transmission scenario. The results demonstrate our ability to maintain a high degree of alignment between the reconstructed content and the original source information, while substantially minimizing the data volume required for transmission. These findings pave the way for further enhancements in communication efficiency and the improvement of Quality of Service. At last, we present future directions for AIGC-SCM studies.

中文翻译：

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利用人工智能生成内容的力量进行语义交流


语义通信（SemCom）被设想为下一代范式，以解决传输数据量不断增加与频谱资源稀缺之间的冲突所带来的挑战。然而，现有的 SemCom 系统面临着可解释性低、模态僵化和重建功能不足等缺点。认识到人工智能生成内容 (AIGC) 技术在内容生成方面的变革能力，本文探索了一种开创性方法，将其集成到 SemCom 中来应对上述挑战。我们采用三层模型来说明所提出的 AIGC 辅助 SemCom (AIGC-SCM) 架构，强调其与现有 SemCom 的明显偏差。在此模型的基础上，我们研究了各种具有增强 SemCom 性能潜力的 AIGC 技术。为了与 SemCom 传达语义的目标保持一致，我们还为 AIGC-SCM 系统引入了新的评估方法。随后，我们探索了我们提出的 AIGC-SCM 可以发挥其潜力的通信场景。为了实际实现，我们构建了详细的集成工作流程，并在虚拟现实图像传输场景中进行了案例研究。结果证明我们有能力保持重建内容和原始源信息之间的高度一致性，同时大大减少传输所需的数据量。这些发现为进一步提高沟通效率和改善服务质量铺平了道路。最后，我们提出了 AIGC-SCM 研究的未来方向。