Al-Rubaye, SabaInalhan, GokhanWarrier, Anirudh Sunil2025-07-022025-07-022024-03https://dspace.lib.cranfield.ac.uk/handle/1826/24132This thesis explores novel methodologies aimed at addressing critical challenges in the integration of Unmanned Aerial Vehicles (UAVs) within heterogeneous networks, particularly in the context of fifth-generation (5G) communication systems. The primary focus revolves around achieving seamless connectivity and mitigating interference to enhance the performance and reliability of UAV operations. As such, this thesis contributes three novel components across these do- mains. The first novel component of this thesis investigates the development of an algorithmic framework grounded in the Received Signal Strength (RSS) criterion for network selection, augmented by the Entropy Weighted Method (EWM) decision-making process. Through comprehensive simulation-level analyses, the efficacy of this approach in facilitating seamless handover (HO) between 5G and long-term evolution (LTE) networks for UAVs is demonstrated. This component represents a significant contribution to addressing the mobility challenges inherent in heterogeneous networks, thus advancing the state-of-the-art in UAV-5G communication. However, ensuring uninterrupted connectivity for UAVs operating in remote or dynamic environments remains a significant hurdle. The second component introduces a novel approach to achieving seamless HO for UAVs transitioning between terrestrial and satellite communication networks, leveraging graph theory to develop a decision-making algorithm aimed at optimising HO decisions. Evaluation through extensive simulations and comparison with existing solutions underscores significant improvements in various performance metrics, such as RSS, Signal-to-Noise Ratio (SNR), throughput, latency, and overall UAV connectivity. The proposed graph-method (GM)-based seamless HO solution represents a pivotal advancement in enabling reliable and uninterrupted communication for UAVs operating in remote and challenging environments, thereby advancing the state-of-the-art in UAV technology. The third component delves into interference mitigation strategies to ensure optimal UAV performance within 5G networks. A novel deep Q learning (DQL) algorithm is proposed to address interference from neighbouring 5G base stations (gNBs). By formulating and solving a Signal-to-Interference and Noise Ratio (SINR) optimisation problem using the DQL algorithm, interference is effectively mitigated, resulting in improved link performance. This Ph.D. research contributes to the advancement of knowledge in the field of UAV-5G integration by presenting innovative solutions to key challenges. The findings pave the way for the seamless incorporation of UAVs into heterogeneous networks, unlocking their full potential across a diverse range of applications, from surveillance to communication infrastructure maintenance and beyond.en© Cranfield University, 2024. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder.Fifth Generation (5G)Unmanned Aerial Vehicles(UAV)mobilityhandoverentropy weighted methodInterference MitigationArtifi- cial IntelligenceDeep Q-Learningvertical handoversatellitegraph methodnon-terrestrial networks (NTN)heterogeneous networksThesis