Large Language Model Enhanced Multi-Agent Systems for 6G Communications

TL;DR

This paper introduces a multi-agent system enhanced with large language models to improve 6G communication tasks through knowledge retrieval, collaborative planning, and solution evaluation, demonstrating effectiveness in a semantic communication case study.

Contribution

It proposes a novel multi-agent framework integrating LLMs with specialized agents for knowledge management, planning, and reflection tailored to 6G communication challenges.

Findings

Enhanced knowledge boundaries for LLMs in 6G communications.

Improved task solution quality through multi-agent collaboration.

Validated effectiveness in a semantic communication case study.

Abstract

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.