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

TL;DR

This paper introduces UAV-FPG, a game-theoretic environment for UAV communication that combines expert knowledge and large language models to improve anti-jamming strategies and path planning.

Contribution

It presents a novel environment model integrating expert knowledge and large language models for UAV communication and anti-jamming strategy development.

Findings

Large language models enhance path planning in dynamic scenarios.

Expert knowledge integration improves frequency selection.

The environment outperforms fixed-path strategies.

Abstract

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.