Abstract

Abstract

Abstract
The increasing complexity of wireless technologies, such as Wi-Fi, presents significant challenges for Rate Adaptation (RA) due to the large configuration space of transmission parameters. While extensive research has been conducted on RA for low-mobility networks, existing solutions fail to adapt in Flying Networks (FNs), where high mobility and dynamic wireless conditions introduce additional uncertainty. We propose Linear Upper Confidence Bound for RA (LinRA), a novel Contextual Bandit-based approach that leverages real-Time link context to optimize transmission rates in predictable FNs, where future trajectories are known. Simulation results demonstrate that LinRA converges 5.2× faster than benchmarks and improves throughput by 80% in Non Line-of-Sight conditions, matching the performance of ideal algorithms. © 2025 Elsevier B.V., All rights reserved.

Abstract
This paper proposes FLUC, a modular framework that integrates open-source Large Language Models (LLMs) with Unmanned Aerial Vehicle (UAV) autopilot systems to enable autonomous control in Flying Networks (FNs). FLUC translates high-level natural language commands into executable UAV mission code, bridging the gap between operator intent and UAV behaviour. 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 UAV positioning confirms FLUC's ability to interpret structured prompts and autonomously execute domain-specific logic, showing its effectiveness in real-time, mission-driven control.