In this paper, we explore a joint source and reconfigurable intelligent surface (RIS)-assisted channel encoding (JSRE) framework for multi-user semantic communications, where a deep neural network (DNN) extracts semantic features for all users and the RIS provides channel orthogonality, enabling a unified semantic encoding-decoding design. We aim to maximize the overall energy efficiency of semantic communications across all users by jointly optimizing the user scheduling, the RIS's phase shifts, and the semantic compression ratio. Although this joint optimization problem can be addressed using conventional deep reinforcement learning (DRL) methods, evaluating semantic similarity typically relies on extensive real environment interactions, which can incur heavy computational overhead during training. To address this challenge, we propose a truncated DRL (T-DRL) framework, where a DNN-based semantic similarity estimator is developed to rapidly estimate the similarity score. Moreover, the user scheduling strategy is tightly coupled with the semantic model configuration. To exploit this relationship, we further propose a semantic model caching mechanism that stores and reuses fine-tuned semantic models corresponding to different scheduling decisions. A Transformer-based actor network is employed within the DRL framework to dynamically generate action space conditioned on the current caching state. This avoids redundant retraining and further accelerates the convergence of the learning process. Numerical results demonstrate that the proposed JSRE framework significantly improves the system energy efficiency compared with the baseline methods. By training fewer semantic models, the proposed T-DRL framework significantly enhances the learning efficiency.

翻译：本文探讨了一种联合信源与可重构智能表面(RIS)辅助信道编码(JSRE)框架，用于多用户语义通信，其中深度神经网络(DNN)提取所有用户的语义特征，RIS提供信道正交性，从而实现统一的语义编解码设计。我们旨在通过联合优化用户调度、RIS相位偏移和语义压缩比，最大化所有用户的整体语义通信能效。尽管该联合优化问题可采用传统深度强化学习(DRL)方法解决，但评估语义相似度通常依赖于大量真实环境交互，这会在训练过程中带来巨大的计算开销。为应对这一挑战，我们提出了一种截断式DRL(T-DRL)框架，其中开发了基于DNN的语义相似度估计器以快速评估相似度得分。此外，用户调度策略与语义模型配置紧密耦合。为利用这一关系，我们进一步提出语义模型缓存机制，存储并复用对应于不同调度决策的微调语义模型。在DRL框架中采用基于Transformer的Actor网络，根据当前缓存状态动态生成动作空间，这避免了冗余重训练，并进一步加速了学习过程的收敛。数值结果表明，与基准方法相比，所提出的JSRE框架显著提升了系统能效。通过训练更少的语义模型，所提出的T-DRL框架显著提升了学习效率。