Pedro Miguel Ribeiro

Abstract

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.

Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly employed to enable wireless communications, serving as communications nodes. In previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). We also developed the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate UAV energy consumption. However, MUAVE was designed to compute the energy consumption for rotary-wing UAVs only. In this paper, we propose eMUAVE, an enhanced version of the MUAVE simulator that enables the evaluation of the energy consumption for both rotary-wing and fixed-wing UAVs. We then use eMUAVE to evaluate the energy consumption of rotary-wing and fixed-wing UAVs in reference and random networking scenarios. The results show that rotary-wing UAVs are typically more energy-efficient than fixed-wing UAVs when following SUPPLY-defined trajectories.

Abstract

Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly used as wireless communications nodes, serving as Wi-Fi Access Points and Cellular Base Stations. To enable energy-efficient access networks, we previously introduced the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of UAVs as Flying Access Points (FAPs) to serve Ground Users (GUs). However, SUPPLY did not address the backhaul link. This paper presents the Simple Gateway Positioning (SGWP) solution, which optimizes the position of a Gateway (GW) UAV to ensure backhaul connectivity in a two-tier network. We integrate SUPPLY for FAP positioning with SGWP for GW placement and evaluate their combined performance under various scenarios involving different GUs' Quality of Service (QoS) requirements and positions. Our results demonstrate that SUPPLY and SGWP can be used jointly in a two-tier network with minimal performance degradation.