[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127973-en":3,"doc-seo-127973-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127973,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Leveraging Machine Learning and Computer Vision for Advanced UAV Communications - PhD Thesis","A wireless communication thesis addresses the demand for flexible deployment, extended coverage, and improved performance in next-generation networks where high mobility and environmental blockages undermine traditional connectivity. It studies UAV-based dynamic base stations for 5G/6G, focusing on reliable communication under latency constraints and the need to balance power consumption with on-device machine learning and computer-vision beamforming. The work proposes CV-ensemble beam management, proactive blockage prediction for mmWave handovers, and vision-aided ML for optimal beam orientation using mmWave and THz, supported by dataset evaluation and advanced antenna modeling.","Ahmad, Iftikhar (2025) Leveraging machine learning and computer vision for advanced UAV communications. PhD thesis.  \n[https://theses.gla.ac.uk/84850/](https://theses.gla.ac.uk/84850/)  \nCopyright and moral rights for this work are retained by the author  \nA copy can be downloaded for personal non-commercial research or study, without prior permission or charge  \nThis work cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author  \nThe content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author  \nWhen referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given  \nEnlighten: Theses  \n[https://theses.gla.ac.uk/](https://theses.gla.ac.uk/)  \n[research-enlighten@glasgow.ac.uk](research-enlighten@glasgow.ac.uk)  \nLeveraging Machine Learning and Computer Vision for Advanced UAV Communications  \nIftikhar Ahmad  \nSubmitted in fulfilment of the requirements for the Degree of Doctor of Philosophy  \nSchool of Engineering College of Science and Engineering University of Glasgow  \nDecember 2024  \nAbstract  \nIn the rapidly developing field of wireless communication, there is a growing demand for technologies that can provide flexible deployment, extended coverage, and enhanced performance in next-generation networks. Traditional networks often struggle with high mobility and environmental blockages, highlighting the need for innovative solutions like Unmanned Aerial Vehicles based (UAV-based) dynamic base stations. UAVs offer a promising solution by functioning as dynamic base stations in 5G and 6G networks, with the potential to address these challenges and improve communication reliability and efficiency.  \nHowever, the integration of UAVs into wireless communication presents significant challenges. Ensuring reliable communication in high-mobility environments, optimizing beam management techniques, predicting blockages in real time, and managing the latency inherent in UAV-assisted networks all require innovative solutions. These challenges are combined by the need to balance power consumption and processing capacity, especially when performing complex tasks such as on-device machine learning and computer vision-based beamforming.  \nThe first study of this dissertation focuses on the challenge of beam management in milimeter wave (mmWave) 5G and beyond networks, where speedy environmental changes in highmobility scenarios degrade signal quality. Previous studies have highlighted the limitations of traditional beamforming approaches, especially in their ability to adjust to dynamic environments. To enhance this, a novel technique is proposed that integrates computer vision (CV) with ensemble learning, employing the \"you look only once\" (YOLO-v5) for precise UAV detection and positioning. By stacking two neural networks to refine a meta-learner, this method achieves approximately 90% top-1 accuracy in K-beam predictions, significantly enhancing the signal-to-noise ratio and improving network performance in high-mobility scenarios.  \nThe second study focuses on the problem of proactive blockage prediction and management in mmWave communications, where maintaining line-of-sight connectivity is necessary. Previous studies have stated that traditional reactive handover methods often result in service disruptions due to unexpected blockages. Computer vision techniques used previously resulted to a 40% improvement in user connectivity by predicting and managing blockages. Extending this concept, the study addresses proactive blockage prediction and management in mmWave communications, employing UAVs not only as base stations but also as proactive agents in handover processes. By leveraging CV to detect potential blockages and monitor user movement, the system facilitates proactive handovers to maintain line-of-sight connectivity. This approach,  \nABSTRACT ii  ","cbCaikLKZPxQbL4C","https://ap.wps.com/l/cbCaikLKZPxQbL4C","pdf",59650661,3,1,135,"English","en",105,"# Contents\n## Abstract\n## List of Tables\n## List of Figures\n## List of Abbreviations\n## List of Publications\n## Acknowledgements\n## Declaration\n## Statement of Copyright\n## 1 Introduction\n### 1.1 Wireless Communication Network Evolution\n#### 1G, The First Generation Mobile Network\n#### 2G, The Second Generation Mobile Network\n#### 3G, The Third Generation Mobile Network\n#### 4G, The Fourth Generation Mobile Network\n#### 5G","[{\"question\":\"Why are UAV-based dynamic base stations important for advanced wireless networks?\",\"answer\":\"They provide flexible deployment and improved coverage while coping with high mobility and environmental blockages that challenge traditional networks.\"},{\"question\":\"What is the main goal of the dissertation’s beam management study?\",\"answer\":\"To improve beam management in mmWave 5G and beyond networks by using computer vision with ensemble learning for accurate UAV detection, positioning, and K-beam predictions.\"},{\"question\":\"How does the thesis improve reliability through blockage prediction and handover management?\",\"answer\":\"It uses computer vision to detect potential blockages and monitor user movement, enabling proactive handovers that maintain line-of-sight connectivity.\"}]","Leveraging Machine Learning and Computer Vision for Advanced UAV Communications - 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