AI 中文总结
针对FANET中现有GPSR协议难以适配动态环境的问题，提出基于LLM的PMKR-GPSR框架，通过参数特定多索引检索和知识引导约束图实现协议一致的路由参数适配，在高移动性场景下提升了数据包投递率并降低了端到端时延。
AI 中文摘要
现有针对飞行自组网（FANET）的基于贪婪周边无状态路由（GPSR）的协议，在高度动态环境下难以适配hello间隔、多路径数量、贪婪转发权重等路由参数。大型语言模型（LLM）作为新兴人工智能技术，具备智能决策潜力，为自适应调整GPSR参数以提升网络性能提供了新机遇。但将LLM应用于GPSR仍面临无关经验检索、缺乏协议约束等挑战。为解决这些问题，本文提出一种用于自适应GPSR优化的参数特定多索引检索与知识引导推理框架（PMKR-GPSR），该基于LLM的框架可实现符合协议要求的路由参数适配。本文设计了参数特定多索引检索机制，为LLM提供与参数相关的经验，同时减少无关信息的干扰；进一步构建知识引导约束图，确保路由参数满足依赖规则和优化约束。仿真结果表明，在高移动性FANET场景下，PMKR-GPSR可实现更高的数据包投递率和更低的端到端时延。
英文摘要
Existing Greedy Perimeter Stateless Routing (GPSR)-based protocols for Flying Ad-Hoc Networks (FANETs) struggle to adapt routing parameters, such as hello interval, multi-path number, and greedy forwarding weights, under highly dynamic environments. As an emerging artificial intelligence technology, large language models (LLMs) show potential for intelligent decision-making, providing new opportunities for adaptive adjustment of GPSR parameters to improve network performance. However, applying LLMs to GPSR remains challenging due to irrelevant experience retrieval and the absence of protocol constraints. To address these issues, we propose a Parameter-Specific Multi-Index Retrieval and Knowledge-Guided Reasoning framework for adaptive GPSR optimization (PMKR-GPSR), an LLM-based framework that enables protocol-consistent routing parameter adaptation. We design a parameter-specific multi-index retrieval mechanism to provide LLMs with parameter-relevant experiences while reducing interference from irrelevant information. We further construct a knowledge-guided constraint graph to enforce that the routing parameters satisfy dependency rules and optimization constraints. Simulation results demonstrate that PMKR-GPSR achieves higher packet delivery ratio and lower end-to-end delay under high-mobility FANETs.
