In this paper, we investigate a generative artificial intelligence (GAI)-assisted semantic communication framework for non-orthogonal multiple access (NOMA)-based image transmissions. Semantic users (SUs) extract cross-modal semantic features from the raw images, which are then used for image recovery by leveraging a GAI model. The GAI enhances the generalization and recovery of semantic image transmissions, while NOMA efficiently allocates transmission capacities to SUs based on their traffic demands. Thus, the semantic extraction and transmission control jointly affect both semantic recovery performance and transmission overhead. We maximize a weighted performance of transmission latency and semantic recovery accuracy by jointly optimizing the semantic feature selection at the semantic level, as well as the receive beamforming and NOMA decoding order at the transmission level. To reduce potential redundancy in semantic features and improve optimization efficiency, we develop an importance-aware and model-driven proximal policy optimization (IM-PPO) framework. Specifically, we quantify and retain high-importance semantic features to enhance the learning efficiency of PPO, while model-based optimization methods are used to adapt the transmission control variables. Numerical results validate that the joint adjustment of the semantic feature selection and the transmission control significantly improves the semantic recovery accuracy and the transmission latency performance. Moreover, the IM-PPO framework effectively leverages the model information to improve the learning efficiency compared to benchmark methods.

翻译：本文研究了一种基于生成式人工智能（GAI）的非正交多址（NOMA）图像传输语义通信框架。语义用户（SU）从原始图像中提取跨模态语义特征，并利用GAI模型进行图像恢复。GAI增强了语义图像传输的泛化与恢复能力，而NOMA则根据用户的业务需求高效分配传输容量。因此，语义提取与传输控制共同影响语义恢复性能与传输开销。我们通过联合优化语义层面的语义特征选择以及传输层面的接收波束成形与NOMA解码顺序，最大化传输时延与语义恢复准确率的加权性能。为减少语义特征的潜在冗余并提升优化效率，我们提出了一种重要性感知的模型驱动近端策略优化（IM-PPO）框架。具体而言，通过量化并保留高重要性语义特征以增强PPO的学习效率，同时采用基于模型的优化方法自适应调整传输控制变量。数值结果验证了语义特征选择与传输控制的联合调整能显著提升语义恢复准确率与传输时延性能。此外，与基准方法相比，IM-PPO框架能有效利用模型信息提升学习效率。