Talk Less, Fly Lighter: Autonomous Semantic Compression for UAV Swarm Communication via LLMs

TL;DR

This paper investigates using Large Language Models to enable semantic compression in UAV swarm communication, reducing bandwidth usage while maintaining task-critical information for autonomous operations.

Contribution

It introduces a novel LLM-driven semantic compression framework for UAV swarms and evaluates its effectiveness across various scenarios and models.

Findings

LLMs can effectively compress semantic information in UAV communications.

The proposed method maintains task accuracy under bandwidth constraints.

Performance varies with environmental complexity and swarm size.

Abstract

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.