用专家知识与大模型构建无人机频段博弈环境，提升抗干扰能力
Frequency Point Game Environment for UAVs via Expert Knowledge and Large Language Model
- 基于博弈论建模敌我无人机频段交互，融合专家知识优化频率选择
- 大模型迭代优化路径规划，动态场景下表现优于固定路径策略
- 适合研究智能抗干扰、无人系统决策的学者和工程师
无人机通过跳频、扩频和自适应干扰抑制等技术显著提升了通信稳定性和安全性。然而，频谱竞争建模、专家知识融合及对手行为预测仍面临挑战。为此，我们提出无人机频点博弈环境UAV-FPG，模拟敌我无人机在通信频段中干扰与反干扰的动态互动。该模型引入先验专家知识库优化频段选择，并利用大语言模型进行路径规划，模拟“强对抗”场景。实验表明，结合专家知识库与大语言模型显著提升性能，尤其在动态环境中，大模型通过迭代交互大幅改善路径规划效果，优于固定路径策略。UAV-FPG为提升无人机通信系统的抗干扰能力和智能决策水平提供了可靠平台。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) have made significant advancements in communication stability and security through techniques such as frequency hopping, signal spreading, and adaptive interference suppression. However, challenges remain in modeling spectrum competition, integrating expert knowledge, and predicting opponent behavior. To address these issues, we propose UAV-FPG (Unmanned Aerial Vehicle - Frequency Point Game), a game-theoretic environment model that simulates the dynamic interaction between interference and anti-interference strategies of opponent and ally UAVs in communication frequency bands. The model incorporates a prior expert knowledge base to optimize frequency selection and employs large language models for path planning, simulating a "strong adversary". Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model, with the latter significantly improving path planning in dynamic scenarios through iterative interactions, outperforming fixed-path strategies. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems.
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