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

arXiv: https://arxiv.org/abs/2312.07850 · HTML: https://mlsanity.com/paper/2312.07850/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 10 of 10)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [Data Distributional Properties Drive Emergent In-Context Learning in Transformers](https://mlsanity.com/paper/2205.05055/) (2022)
- [Large Language Models Empowered Autonomous Edge AI for Connected Intelligence](https://mlsanity.com/paper/2307.02779/) (2023)
- [Large AI Model-Based Semantic Communications](https://mlsanity.com/paper/2307.03492/) (2023)
- [Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions](https://mlsanity.com/paper/2308.06250/) (2023)
- [Semantic Communication Meets Edge Intelligence](https://mlsanity.com/paper/2202.06471/) (2022)
- [A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMs](https://mlsanity.com/paper/2311.12893/) (2023)

## Built on by (most cited first, 30 of 53)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends](https://mlsanity.com/paper/2409.14457/) (2024)
- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
- [Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection](https://mlsanity.com/paper/2405.11002/) (2024)
- [6G WavesFM: A Foundation Model for Sensing, Communication, and Localization](https://mlsanity.com/paper/2504.14100/) (2025)
- [Large Generative Model Assisted 3D Semantic Communication](https://mlsanity.com/paper/2403.05783/) (2024)
- [Visual Language Model based Cross-modal Semantic Communication Systems](https://mlsanity.com/paper/2407.00020/) (2024)
- [Can LLMs Understand Computer Networks? Towards a Virtual System Administrator](https://mlsanity.com/paper/2404.12689/) (2024)
- [Personalized Wireless Federated Learning for Large Language Models](https://mlsanity.com/paper/2404.13238/) (2024)
- [Maris: A Formally Verifiable Privacy Policy Enforcement Paradigm for Multi-Agent Collaboration Systems](https://mlsanity.com/paper/2505.04799/) (2025)
- [ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks](https://mlsanity.com/paper/2601.19607/) (2026)
- [EdgeShard: Efficient LLM Inference via Collaborative Edge Computing](https://mlsanity.com/paper/2405.14371/) (2024)
- ["X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems](https://mlsanity.com/paper/2507.19657/) (2025)
- [BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Web](https://mlsanity.com/paper/2508.13787/) (2025)
- [Agentic AI Empowered Intent-Based Networking for 6G](https://mlsanity.com/paper/2601.06640/) (2026)
- [Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness](https://mlsanity.com/paper/2409.10825/) (2024)
- [SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework](https://mlsanity.com/paper/2503.08726/) (2025)
- [ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications](https://mlsanity.com/paper/2505.19983/) (2025)
- [Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges](https://mlsanity.com/paper/2410.18125/) (2024)
- [Large Generative Model-assisted Talking-face Semantic Communication System](https://mlsanity.com/paper/2411.03876/) (2024)
- [Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model](https://mlsanity.com/paper/2412.20820/) (2024)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks](https://mlsanity.com/paper/2406.04276/) (2024)
- [Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications](https://mlsanity.com/paper/2505.18194/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Agentic TinyML for Intent-aware Handover in 6G Wireless Networks](https://mlsanity.com/paper/2508.09147/) (2025)
- [Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI](https://mlsanity.com/paper/2509.08151/) (2025)
- [Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networks](https://mlsanity.com/paper/2512.09485/) (2025)
- [Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models](https://mlsanity.com/paper/2601.07978/) (2026)
- [Personalized Federated Learning for Generative AI-Assisted Semantic Communications](https://mlsanity.com/paper/2410.02450/) (2024)

## Abstract (arXiv)

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.