Frequency Point Game Environment for UAVs via Expert Knowledge and Large Language Model

Highlights: What are the main findings? We propose UAV-FPG, a novel reinforcement learning-based game environment that simulates dynamic signal interference and anti-interference confrontations between UAVs. Within UAV-FPG, the LLM-based opponent planner provides a practical, gradient-free mechanism to generate diverse, feedback-conditioned trajectories and often yields higher opponent rewards than fixed-path baselines, thereby strengthening simulator-side stress tests of ally anti-jamming policies (without implying real-world superiority). What are the implications of the main findings? The UAV-FPG environment serves as a high-fidelity platform for systematically developing and validating anti-jamming decision-making strategies in complex electromagnetic scenarios Our simulation results suggest that LLM-driven opponents can act as a stronger and more adaptive adversary within UAV-FPG, providing a practical, gradient-free way to generate diverse trajectories in high-dimensional decision spaces. 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 episode-level opponent trajectory generation and planning within UAV-FPG, serving as an operationally more challenging simulator adversary for stress-testing anti-jamming policies under our evaluation protocol. Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model: relative to fixed-path baselines, iterative feedback-conditioned LLM planning tends to generate more adaptive trajectories and achieve higher opponent rewards in UAV-FPG. These findings are confined to the proposed simulation environment and are not intended as general claims about real-world jamming capability or onboard planning performance. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems.