  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.
