[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120720-en":3,"doc-seo-120720-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120720,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning Empowered - Reconfi gurable Intelligent Surfaces - Doctor of Philosophy Thesis","Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), are studied as key auxiliary equipment for future wireless networks by dynamically modifying propagation through controllable reflective elements. RISs steer signal direction, amplitude, and phase shift, creating cascaded channels to enhance user communication. Compared with active relays, RISs offer flexible deployment, economical cost, and high energy efficiency. This thesis proposes a 6G paradigm using RISs to build smart radio environments and introduces STAR-RIS to enable 360° coverage.","Machine Learning Empowered  \nRecon􀀌gurable Intelligent Surfaces  \nby  \nRuikang Zhong  \nSupervisors : Prof. Yue Chen, Dr. Yuanwei Liu Independent Assessor: Prof. Kok Keong Chai  \nDoctor of Philosophy  \nSchool of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom  \nMay 2023  \nAcknowledgments  \nForemost, I would like to thank Prof. Yue Chen, Dr. Yuanwei Liu, and independent assessor Prof. Kok Keong Chai for their unwavering support of my Ph.D study. With patience and vast expertise, they not only gave me helpful technical advice and constructive comments on my academic undertakings and directions, but they also o􀀋ered meindispensable advice for daily life. Their expertise and encouragement have been crucial in shaping my research and helping me overcome obstacles along the way.  \nI would like to thank all my collaborators: Prof. Lingyang Song (Peking University), Prof. Xianbin Wang (Western University), Prof. Zhu Han (University of Houston), Prof. Lajos Hanzo (University of Southampton), Prof. Ping Zhang (Beijing University of Posts and Telecommunications), Prof. Jianhua Zhang (Beijing University of Postsand Telecommunications), and Dr. Mona Jaber (QMUL) for their helpful suggestions and comments on my research.  \nI would also like to thank Dr. Xiao Liu, Dr. Xidong Mu, Dr. Gui Zhou, Dr. Zhong Yang, Dr. Tianwei Hou, Dr. Wenqiang Yi, Dr. Syed Khurram Mahmud, Dr. Yixuan Zou, Dr. Zhishu Qu, Yanling Hao, Chao Zhang, Jiaqi Xu, Xinyu Gao, Na Xue, Ziyi Xie, Yuqin Liu, Zhixiong Chen, Yimeng Zhang, Haochen Li, Zhaolin Wang, Zheng Zhang, Meng Zhang, ZhaomingHu, Suyu Lyu, QianGao, Kangda Zhi, Na Yan, Tuo Wu and all my colleges and friends in the communication systems research group and antenna group at the Queen Mary University of London, for their constant encouragement and kind help. I really have had wonderful memories in my Ph.D life and study.  \nI would like to express my deepest gratitude to my beloved parents. I would also like to express my pure love to my wife, Qilei Wang.  \nAbstract  \nRecon􀀌gurable intelligent surfaces (RISs) or known as intelligent re􀀍ecting surfaces (IRSs) have emerged as potential auxiliary equipment for future wireless networks, which attracts extensive research interest in their characteristics, applications, and potential. RIS is a panel surface equipped with a number of re􀀍ective elements, which can arti􀀌 -cially modify the propagation environment of the electrogenic signals. Speci􀀌cally, RISshave the ability to precisely adjust the propagation direction, amplitude, and phaseshift of the signals, providing users with a set of cascaded channels in addition to direct channels, and thereby improving the communication performances for users. Compared with other candidate technologies such as active relays, RIS has advantages in terms of 􀀍exible deployment, economical cost, and high energy e􀀎ciency. Thus, RISs have been considered a potential candidate technique for future wireless networks.  \nIn this thesis, a wireless network paradigm for the sixth generation (6G) wireless networks is proposed, where RISs are invoked to construct smart radio environments (SRE) to enhance communication performances for mobile users. In addition, beyond the conventional reselecting-only RIS, a novel model of RIS is originally proposed, namely, simultaneous transmitting and re􀀍ecting recon􀀌gurable intelligent surface (STAR-RIS) . The STAR-RIS splits the incident signal into transmitted and re􀀍ected signals, making full utilization of them to generate 360􀀎 coverage around the STAR-RIS panel, improving the coverage of the RIS. In order to fully exert the channel domination and beamforming ability of the RISs and STAR-RSIs to construct SREs, several machine learning algorithms, including deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) approaches are developed to optimize the communication performance in respect of sum data rate or energy e􀀎ciency for the RIS-a","cbCaioRAoNKDxU3P","https://ap.wps.com/l/cbCaioRAoNKDxU3P","pdf",5574143,1,200,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n# List of Tables\n# List of Abbreviations\n# Chapter 1\n## Intr","[{\"question\":\"What are reconfigurable intelligent surfaces (RISs) and how do they help wireless networks?\",\"answer\":\"RISs are panel surfaces with reflective elements that artificially control signal propagation by adjusting direction, amplitude, and phase. They create cascaded channels that improve communication performance for users.\"},{\"question\":\"How does the thesis extend beyond conventional reselecting-only RIS?\",\"answer\":\"It proposes STAR-RIS, which simultaneously transmits and reflects the incident signal. This design enables 360° coverage around the STAR-RIS panel and improves coverage for the wireless environment.\"},{\"question\":\"Which machine learning approaches are developed to optimize RIS-assisted networks?\",\"answer\":\"The thesis develops deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) methods. These algorithms optimize metrics such as sum data rate or energy efficiency while supporting beamforming and deployment decisions.\"}]","Machine Learning Empowered - Reconfi gurable Intelligent Surfaces - Doctor of Philosophy Thesis | PDF",1785731702,504,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-empowered-reconfigurable-intelligent-surfaces-doctor-of-philosophy-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@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-empowered-reconfigurable-intelligent-surfaces-doctor-of-philosophy-thesis/120720/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What are reconfigurable intelligent surfaces (RISs) and how do they help wireless networks?","Question",{"text":75,"@type":76},"RISs are panel surfaces with reflective elements that artificially control signal propagation by adjusting direction, amplitude, and phase. They create cascaded channels that improve communication performance for users.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis extend beyond conventional reselecting-only RIS?",{"text":80,"@type":76},"It proposes STAR-RIS, which simultaneously transmits and reflects the incident signal. This design enables 360° coverage around the STAR-RIS panel and improves coverage for the wireless environment.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are developed to optimize RIS-assisted networks?",{"text":84,"@type":76},"The thesis develops deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) methods. 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