In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by the complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregates them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.

翻译：为响应6G全球通信需求，卫星通信网络已成为关键解决方案。然而，卫星通信网络的大规模发展受限于复杂的系统模型，其建模对海量用户具有挑战性。此外，卫星与用户间的传输干扰严重影响通信性能。为解决这些问题，本文开发了用于模型构建的生成式人工智能（AI）代理，随后应用专家混合（MoE）方法设计传输策略。具体而言，我们利用大语言模型（LLMs）构建交互式建模范式，并采用检索增强生成（RAG）技术提取支持数学建模的卫星领域专家知识。之后，通过整合多个专业组件的专长，我们提出一种MoE-近端策略优化（PPO）方法来解决所构建的问题。每个专家通过其自身网络的专门训练，优化其擅长的优化变量，随后通过门控网络进行聚合以执行联合优化。仿真结果验证了采用生成式代理进行问题构建的准确性和有效性。此外，所提出的MoE-PPO方法在解决所构建问题方面相较于其他基准方法的优越性得到了证实。MoE-PPO对各种定制化建模问题的适应性也得到了验证。