A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks

--This paper proposes FLUC, a modular framework that integrates open-source Large Language Models (LLMs) with Unmanned Aerial V ehicle (UA V) autopilot systems to enable autonomous control in Flying Networks (FNs). FLUC is evaluated using three open-source LLMs - Qwen 2.5, Gemma 2, and LLaMA 3.2 - across scenarios involving code generation and mission planning. Results show that Qwen 2.5 excels in multi-step reasoning, Gemma 2 balances accuracy and latency, and LLaMA 3.2 offers faster responses with lower logical coherence. A case study on energy-aware UA V positioning confirms FLUC's ability to interpret structured prompts and autonomously execute domain-specific logic, showing its effectiveness in real-time, mission-driven control. The demand for adaptable and reliable wireless communications systems has led to the adoption of Flying Networks (FNs), where Unmanned Aerial V ehicles (UA Vs) act as airborne communications nodes. FNs provide on-demand network coverage in scenarios where terrestrial infrastructure is infeasible or insufficient, such as disaster response, large-scale events, and remote rural areas (see Figure 1).