用大模型增强强化学习，优化6G无线网络
Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization
- 用大模型改进状态表示与语义提取，提升多智能体决策能力
- 在无人机-卫星网络中实现服务迁移、路由与拓扑生成优化
- 适合研究6G网络智能优化的学者和工程师
未来无线网络面临用户需求多样性和6G技术兴起带来的挑战。强化学习虽具潜力，但常因高维状态空间和复杂环境导致计算开销大、智能体分布不均且结果不一致。大语言模型凭借预训练知识和推理能力，可有效增强强化学习在6G无线网络中的表现。本文探讨了融合大模型的强化学习框架，重点分析其在物理层、数据链路层、网络层、传输层和应用层的应用潜力。提出一种大模型辅助的状态表示与语义提取方法，用于改进多智能体强化学习。该方法应用于无人机-卫星网络的服务迁移、请求路由及拓扑图生成。案例研究显示，该框架能有效实现无线网络优化。最后，展望了该方向的未来研究路径。
原文摘要 · Abstract (English)
Enhancing future wireless networks presents a significant challenge for networking systems due to diverse user demands and the emergence of 6G technology. While reinforcement learning (RL) is a powerful framework, it often encounters difficulties with high-dimensional state spaces and complex environments, leading to substantial computational demands, distributed intelligence, and potentially inconsistent outcomes. Large language models (LLMs), with their extensive pretrained knowledge and advanced reasoning capabilities, offer promising tools to enhance RL in optimizing 6G wireless networks. We explore RL models augmented by LLMs, emphasizing their roles and the potential benefits of their synergy in wireless network optimization. We then examine LLM-enabled RL across various protocol layers: physical, data link, network, transport, and application layers. Additionally, we propose an LLM-assisted state representation and semantic extraction to enhance the multi-agent reinforcement learning (MARL) framework. This approach is applied to service migration and request routing, as well as topology graph generation in unmanned aerial vehicle (UAV)-satellite networks. Through case studies, we demonstrate that our framework effectively performs optimization of wireless network. Finally, we outline prospective research directions for LLM-enabled RL in wireless network optimization.
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