The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e.g., network optimization and management by allowing users to input task requirements to LLMs by nature language. However, directly applying native LLMs in 6G encounters various challenges, such as a lack of private communication data and knowledge, limited logical reasoning, evaluation, and refinement abilities. Integrating LLMs with the capabilities of retrieval, planning, memory, evaluation and reflection in agents can greatly enhance the potential of LLMs for 6G communications. To this end, we propose a multi-agent system with customized communication knowledge and tools for solving communication related tasks using natural language, comprising three components: (1) Multi-agent Data Retrieval (MDR), which employs the condensate and inference agents to refine and summarize communication knowledge from the knowledge base, expanding the knowledge boundaries of LLMs in 6G communications; (2) Multi-agent Collaborative Planning (MCP), which utilizes multiple planning agents to generate feasible solutions for the communication related task from different perspectives based on the retrieved knowledge; (3) Multi-agent Evaluation and Reflecxion (MER), which utilizes the evaluation agent to assess the solutions, and applies the reflexion agent and refinement agent to provide improvement suggestions for current solutions. Finally, we validate the effectiveness of the proposed multi-agent system by designing a semantic communication system, as a case study of 6G communications.

翻译：大型语言模型（LLM）的快速发展为6G通信带来了巨大机遇，例如允许用户通过自然语言向LLM输入任务需求，实现网络优化与管理。然而，直接将原生LLM应用于6G场景面临多重挑战，例如缺乏私有通信数据和知识、逻辑推理能力有限、评估与精炼能力不足。将LLM与智能体的检索、规划、记忆、评估与反思能力相结合，能够显著提升LLM在6G通信中的潜力。为此，我们提出了一种面向6G通信任务的多智能体系统，该系统集成了定制化通信知识与工具，支持通过自然语言解决通信相关任务，包含三个组件：（1）多智能体数据检索（MDR），利用压缩智能体与推理智能体对知识库中的通信知识进行精炼与总结，扩展LLM在6G通信领域的知识边界；（2）多智能体协同规划（MCP），基于检索到的知识，利用多个规划智能体从不同视角为通信相关任务生成可行解决方案；（3）多智能体评估与反思（MER），利用评估智能体对解决方案进行评价，并引入反思智能体与精炼智能体为当前方案提供改进建议。最后，我们以设计语义通信系统作为6G通信案例，验证了所提多智能体系统的有效性。