[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127966-en":3,"doc-seo-127966-105":30,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127966,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning Solutions for Intelligent Reflecting Surface-Assisted Communications - Doctor of Philosophy Thesis","Intelligent reflecting surface (IRS) is a key enabling technology for future wireless systems, but conventional optimization for IRS-assisted communications often incurs high computational complexity, particularly in MIMO settings. This thesis proposes machine learning (ML) approaches for IRS-assisted MIMO with the goal of maximizing spectral efficiency and/or secrecy rate. A deep learning model sequentially estimates the IRS reflection matrix and hybrid beamformers, while a deep reinforcement learning method based on DDPG optimizes IRS phase shifts to achieve both efficiency and security gains. Simulations validate performance.","Machine Learning Solutions for Intelligent Reflecting Surface-Assisted Communications  \nby  \nKenneth Chinonso IKEAGU  \nA thesis submitted in fulfilment of the requirements for the degree  \nof  \nDoctor of Philosophy  \nInstitute of Signals, Sensors and Systems School of Engineering and Physical Sciences HERIOT-WATT UNIVERSITY  \nJune, 2024  \nThe copyright in this thesis is owned by the author. Any quotation from the thesis or use of any of the information contained in it must acknowledge this thesis as the source of the quotation or information.  \nAbstract  \nIntelligent reflecting surface (IRS) has been recognised as a promising technology for future wireless systems. Consequently, several conventional optimisation schemes have been proposed for solving IRS-assisted communication problems. However, these conventional optimisation methods come with high computational complexity, especially for the multiple-input-multiple-output (MIMO) communication systems. Fortunately, it has been shown in the literature that machine learning (ML)-based schemes have been successful in tackling diverse problems in the wireless communications domain with reduced complexity. Motivated by this, in this Thesis, ML-based solutions are proposed for IRS-assisted MIMO communication systems aiming to maximise the spectral efficiency and/or secrecy rate of the proposed systems. First, a deep learning (DL) based model is proposed to jointly optimise the hybrid beamformers and the IRS reflection matrix for an IRSaided MIMO communication system wherein a two-stage neural network is trained sequentially to estimate the IRS matrix (from a formulated effective channel), as well as the hybrid beamformers while aiming to maximise the spectral efficiency of the system. In the second and final contributions, a deep reinforcement learning (DRL) algorithm is proposed to optimise the phase shift of the reflecting elements of an IRS for IRS-assisted MIMO communication systems aiming to maximise the system’s spectral efficiency and secrecy rate respectively. Specifically, the deep deterministic policy gradient (DDPG) framework is proposed to tackle the formulated non-convex problems following its success in handling high-dimensional continuous action spaces and tackling non-convex optimisation problems. Numerical simulations are provided to validate the competitive performance of the proposed ML-based solutions.  \nDedicated to my family and friends for their unwavering love and support.  \nAcknowledgements  \nI am deeply grateful to my supervisors Dr. Yuan Ding, Dr. Chaoyun Song, Dr. Muhammad Khandaker, and Prof. Mathini Sellutherai for their incredible support throughout this journey. I also use this opportunity to extend my sincerest gratitude to the Department of Electronic Engineering, University of Nigeria, Nsukka for their encouragement during this period. To my colleagues and researchers in the Microwave and Antenna Engineering Group at Heriot-Watt University; I am immensely grateful for all your support, input, and recommendations during this journey. To my friends in Edinburgh, who made my time and life in Edinburgh avery enjoyable and memorable one, I say a very big thank you to you all. To my parents, siblings, cousins, uncles, and aunts who have shown me love and kindness in so many ways, your support and encouragement are highly appreciated. And to God Almighty, for always looking out for me, I am eternally grateful.  \nInclusion of Published Works Form  \nPlease note you are only required to complete this form if your thesis contains published works. If this is the case, please include this form within your thesis before submission.  \nDeclaration  \nThis thesis contains one or more multi-author published works. I hereby declare that the contributions of each author to these publications is as follows:  \n\n| Citation details | K. Ikeagu , M. R. A. Khandaker, C. Song and Y. Ding,“Deep Learning-Based Hybrid Beamforming Design for IRS-Aided MIMO Communication” in IEEE","cbCaigveTglzYskh","https://ap.wps.com/l/cbCaigveTglzYskh","pdf",8782174,1,145,"English","en",105,"# Abstract\n# Acknowledgements\n# Contents\n# List of Figures\n# List of Tables\n# Abbreviations\n# Publications\n# 1 Introduction\n## 1.1 Motivation\n## 1.2 Contributions of the Thesis\n## 1.3 Organisation of the Thesis\n# 2 Background Study and Literature Review\n## 2.1 Intelligent Reflecting Surface (IRS)\n## 2.2 Hybrid Beamforming (HBF) in Wireless Networks\n## 2.3 Physical Layer Security for IRS-Assisted Communications\n## 2.4 Machine Learning (ML) for IRS-assisted Communications","[{\"question\":\"What optimization problems does the thesis address for IRS-assisted MIMO communications?\",\"answer\":\"It targets IRS-assisted MIMO problems where conventional schemes have high computational complexity, aiming to maximize spectral efficiency and secrecy rate through ML-based optimization.\"},{\"question\":\"How does the proposed deep learning model optimize the IRS-assisted system?\",\"answer\":\"It uses a two-stage neural network trained sequentially to estimate the IRS reflection matrix from a formulated effective channel and then estimate the hybrid beamformers, while maximizing spectral efficiency.\"},{\"question\":\"What deep reinforcement learning framework is used to optimize IRS phase shifts?\",\"answer\":\"The thesis proposes a deep deterministic policy gradient (DDPG) framework to handle the formulated non-convex optimization and continuous high-dimensional action space for maximizing spectral efficiency and secrecy rate.\"}]","Machine Learning Solutions for Intelligent Reflecting Surface-Assisted Communications - Doctor of Philosophy Thesis | PDF",1785943418,365,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-solutions-for-intelligent-reflecting-surface-assisted-communications-doctor-of-philosophy-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-solutions-for-intelligent-reflecting-surface-assisted-communications-doctor-of-philosophy-thesis/127966/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What optimization problems does the thesis address for IRS-assisted MIMO communications?","Question",{"text":76,"@type":77},"It targets IRS-assisted MIMO problems where conventional schemes have high computational complexity, aiming to maximize spectral efficiency and secrecy rate through ML-based optimization.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed deep learning model optimize the IRS-assisted system?",{"text":81,"@type":77},"It uses a two-stage neural network trained sequentially to estimate the IRS reflection matrix from a formulated effective channel and then estimate the hybrid beamformers, while maximizing spectral efficiency.",{"name":83,"@type":74,"acceptedAnswer":84},"What deep reinforcement learning framework is used to optimize IRS phase shifts?",{"text":85,"@type":77},"The thesis proposes a deep deterministic policy gradient (DDPG) framework to handle the formulated non-convex optimization and continuous high-dimensional action space for maximizing spectral efficiency and secrecy rate.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]