用大模型压缩无人机群通信，省带宽还保关键信息
Talk Less, Fly Lighter: Autonomous Semantic Compression for UAV Swarm Communication via LLMs
- 用大模型自动压缩任务语义，减少群内通信量
- 在多跳、带宽受限下仍保持协作效率
- 验证了主流大模型在复杂环境中的适应性
大语言模型在无人系统中的应用显著提升了无人机集群的语义理解与自主任务执行能力。然而，有限的通信带宽与高频交互需求给集群内语义信息传输带来严峻挑战。本文探索基于大模型的无人机集群自主语义压缩通信可行性，旨在降低通信负载的同时保留关键任务语义。为此，构建了四种不同环境复杂度的二维仿真场景，设计融合系统提示与任务指令提示的通信-执行流水线。在此基础上，系统评估九种主流大模型在不同场景下的语义压缩性能，并通过消融实验分析环境复杂度与集群规模对其适应性与稳定性的影响。实验结果表明，基于大模型的无人机集群在带宽受限和多跳链路条件下具备实现高效协同通信的潜力。
原文摘要 · Abstract (English)
The rapid adoption of Large Language Models (LLMs) in unmanned systems has significantly enhanced the semantic understanding and autonomous task execution capabilities of Unmanned Aerial Vehicle (UAV) swarms. However, limited communication bandwidth and the need for high-frequency interactions pose severe challenges to semantic information transmission within the swarm. This paper explores the feasibility of LLM-driven UAV swarms for autonomous semantic compression communication, aiming to reduce communication load while preserving critical task semantics. To this end, we construct four types of 2D simulation scenarios with different levels of environmental complexity and design a communication-execution pipeline that integrates system prompts with task instruction prompts. On this basis, we systematically evaluate the semantic compression performance of nine mainstream LLMs in different scenarios and analyze their adaptability and stability through ablation studies on environmental complexity and swarm size. Experimental results demonstrate that LLM-based UAV swarms have the potential to achieve efficient collaborative communication under bandwidth-constrained and multi-hop link conditions.
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