[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119906-en":3,"doc-seo-119906-105":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},119906,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Distributed Network Control Using Machine Learning","Doctoral dissertation investigating distributed network control methods powered by machine learning across multiple problem domains. The work presents data-driven dynamic path selection for real-time video, including prediction, probing, and online channel selection with performance evaluation. It further develops frequency-based multi-task learning with attention for fault detection in power systems. Additional contributions include federated deep reinforcement learning for distributed control of NextG wireless networks and a survey of AI/ML use cases in O-RAN from academia to industry, followed by delay-aware RAN slicing personalization for next-generation architectures.","UC Irvine  \nUC Irvine Electronic Theses and Dissertations  \nTitle  \nDistributed Network Control Using Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/4sz5h3b4](https://escholarship.org/uc/item/4sz5h3b4)  \nAuthor  \nTehrani, Peyman  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA,  \nIRVINE  \nDistributed Network Control Using Machine Learning  \nDISSERTATION  \nsubmitted in partial satisfaction of the requirements  \nfor the degree of  \nDOCTOR OF PHILOSOPHY  \nin Networked Systems  \nby  \nPeyman Tehrani  \nDissertation Committee: Professor Marco Levorato, Chair Professor Scott Jordan  \nAssistant Professor Faisal Nawab  \nChapter 2 © Institute of Electrical and Electronics Engineers (IEEE Publishing) Chapter 3 © Institute of Electrical and Electronics Engineers (IEEE Publishing) Chapter 4 © Institute of Electrical and Electronics Engineers (IEEE Publishing) All other materials © 2023 Peyman Tehrani  \nDEDICATION  \nI dedicate my PhD thesis to Kian Pirfalak, an innocent young talented soul, who was  \ndreaming to become an engineer one day ...  \nTABLE OF CONTENTS  \nPage  \nLIST OF FIGURES vi  \nLIST OF TABLES viii  \nACKNOWLEDGMENTS ix  \nVITA x  \nABSTRACT OF THE DISSERTATION xii  \n1 Introduction 1  \n1.1 Motivation: Incorporating AI in Networks ................... 1  \n1.2 Dissertation Contributions & Overview ..................... 2  \n2 Data-driven Dynamic Path Selection for Real-time Video Applications 5  \n2.1 Abstract ...................................... 5  \n2.2 Introduction .................................... 6  \n2.3 Preliminaries ................................... 8  \n2.3.1 Scenario .................................. 9  \n2.3.2 Selection Problem ............................. 11  \n2.3.3 Prediction ................................. 11  \n2.3.4 Probing .................................. 13  \n2.4 Network Environment and Dataset ....................... 14  \n2.5 Prediction Framework .............................. 18  \n2.5.1 Feature Analysis ............................. 18  \n2.5.2 Classifier .................................. 21  \n2.5.3 Channel Selection ............................. 24  \n2.6 Performance Evaluation ............................. 24  \n2.6.1 Offline Classifier Performance ...................... 25  \n2.6.2 Online Channel Selection ......................... 25  \n2.7 Related Work ................................... 30  \n2.8 Conclusions .................................... 32  \n3 Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems 33  \n3.1 Abstract ...................................... 33  \n3.2 Introduction .................................... 34  \n3.3 Dataset Description and Analysis ........................ 36  \n3.3.1 Time Domain Analysis .......................... 38  \n3.4 Frequency Based Feature Processing ...................... 40  \n3.4.1 Frequency Based Clustering ....................... 42  \n3.5 Classifier ...................................... 45  \n3.6 Performance Evaluation ............................. 47  \n3.7 Conclusions .................................... 50  \n4 Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks 51  \n4.1 Introduction .................................... 52  \n4.2 Literature Review ................................. 54  \n4.3 System Model and Problem Formulation .................... 54  \n4.4 Federated Deep Reinforcement Learning .................... 57  \n4.4.1 RL Formulation .............................. 58  \n4.4.2 Federated Learning Formulation ..................... 60  \n4.4.3 Federated Deep Q Network ....................... 60  \n4.4.4 Federated Deep Policy Gradient ..................... 62  \n4.5 Results ....................................... 63  \n4.6 Conclusions .................................... 67  \n5 Survey of AI/ML Use-cases In O-RAN: From Academia T","cbCaisz8uNmCiaKJ","https://ap.wps.com/l/cbCaisz8uNmCiaKJ","pdf",5131089,1,188,"English","en",105,"# 1 Introduction\n## 1.1 Motivation: Incorporating AI in Networks\n## 1.2 Dissertation Contributions & Overview\n# 2 Data-driven Dynamic Path Selection for Real-time Video Applications\n## 2.1 Abstract\n## 2.2 Introduction\n## 2.3 Preliminaries\n## 2.4 Network Environment and Dataset\n## 2.5 Prediction Framework\n## 2.6 Performance Evaluation\n## 2.7 Related Work\n## 2.8 Conclusions\n# 3 Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems\n## 3.1 Abstract\n## 3.2 Introduction\n## 3.3 Dataset Description and Analysis\n## 3.4 Frequency Based Feature Processing\n## 3.5 Classifier\n## 3.6 Performance Evaluation\n## 3.7 Conclusions\n# 4 Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks\n## 4.1 Introduction\n## 4.2 Literature Review\n## 4.3 System Model and Problem Formulation\n## 4.4 Federated Deep Reinforcement Learning\n## 4.5 Results\n## 4.6 Conclusions\n# 5 Survey of AI/ML Use-cases In O-RAN: From Academia To Industry\n## 5.1 Abstract\n## 5.2 Introduction\n## 5.3 Surveys\n## 5.4 O-RAN Architecture Overview\n## 5.5 O-RAN Testbeds & Frameworks\n## 5.6 Prototype\n## 5.7 Traffic Steering\n## 5.8 Resource allocation\n## 5.9 Spectrum Sensing\n## 5.10 Automation and Orchestration\n## 5.11 Traffic Prediction\n## 5.12 Security & Anomaly Detection\n## 5.13 Slicing\n## 5.14 Access Control & Load Balancing\n## 5.15 Energy Efficiency\n## 5.16 MIMO & Beam forming\n## 5.17 Medical Applications\n## 5.18 Federated Learning\n## 5.19 Other Use Cases\n## 5.20 ML Based xApp and rApp in Industry\n## 5.21 Conclusion\n# 6 Deep Reinforcement Learning and Reward Based Personalization for NextG Delay Aware RAN Slicing in O-RAN\n## 6.1 abstract\n## 6.2 Introduction\n## 6.3 System Model\n## 6.4 Problem Formulation\n## 6.5 Deep Reinforcement Learning","[{\"question\":\"What is the dissertation’s main theme and goal?\",\"answer\":\"The dissertation focuses on distributed network control using machine learning, targeting real-time, wireless, and power-system scenarios. It develops learning-based mechanisms for decision-making and control under different constraints.\"},{\"question\":\"How does the dissertation address real-time video path selection?\",\"answer\":\"It proposes a data-driven dynamic path selection approach that includes prediction and probing, followed by online channel selection. Performance is evaluated for both offline classifier behavior and online decisions.\"},{\"question\":\"What role does federated deep reinforcement learning play in NextG wireless networks?\",\"answer\":\"It formulates a federated deep reinforcement learning framework for distributed control in NextG wireless networks. The method integrates federated learning with deep Q and deep policy gradient components and reports results.\"}]","Distributed Network Control Using Machine Learning | PDF",1785726931,474,{"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},"distributed-network-control-using-machine-learning","",{"@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/distributed-network-control-using-machine-learning/119906/",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","2026-08-05","2026-08-03",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 is the dissertation’s main theme and goal?","Question",{"text":76,"@type":77},"The dissertation focuses on distributed network control using machine learning, targeting real-time, wireless, and power-system scenarios. It develops learning-based mechanisms for decision-making and control under different constraints.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the dissertation address real-time video path selection?",{"text":81,"@type":77},"It proposes a data-driven dynamic path selection approach that includes prediction and probing, followed by online channel selection. Performance is evaluated for both offline classifier behavior and online decisions.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does federated deep reinforcement learning play in NextG wireless networks?",{"text":85,"@type":77},"It formulates a federated deep reinforcement learning framework for distributed control in NextG wireless networks. The method integrates federated learning with deep Q and deep policy gradient components and reports results.","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"]