[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121787-en":3,"doc-seo-121787-105":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},121787,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Physical Layer Jamming detection - a Machine Learning Approach","The thesis presents a laboratory methodology that uses machine learning to analyze the physical-layer characteristics of a wireless cellular channel (layer 1) to detect the presence of a jamming signal overlapping with cellular communications. The goal is a binary classifier that labels the channel as normal or corrupted by an attacker. Experiments employ Software Defined Radios on both user and attacker sides, with theoretical background and technical discussion of results, including evaluation using channel observations.","University of Padova  \nDepartment of Information Engineering Master Thesis in Ict For Internet and Multimedia  \nPhysical Layer Jamming detection: a Machine Learning Approach  \nSupervisor Master Candidate  \nProf. Stefano Tomasin Matteo Varotto  \nUniversity of Padova  \nCo-supervisor Student ID  \nProf. Stefan Valentin 2037034  \nAcademic Year  \n2022-2023  \nx  \nii  \n“Milano non è Milan, Italia è Milan”  \n—Zlatan Ibrahimović  \niv  \nAbstract  \nThis thesis aims to illustrate the laboratory experience of using machine learning techniques to analyze the physical characteristics (i.e: International Standard Organization layer 1) of a wireless cellular channel in order to detect the presence of a jamming signal overlapped to a cellular communication signal.  \nThus, the expected outcome of the project is to construct a binary classifier, which takes as input information on the wireless channel and outputs the state of the channel through a binary classification: that is, whether the channel is in a state recognized as normal or whether it has been corrupted by the presence of an attacker.  \nLab experiments were carried out using Software Defined Radios, both user-side and attackerside. Therefore, the methodologies used to conduct these experiments are explained, specifying the theoretical background and commenting from a technical point of view on the results obtained.  \nvi  \nContents  \nAbstract v  \nList of tables ix  \nListing of acronyms ix  \n1 Introduction 1  \n2 Cellular Communications 3  \n2.1 Channel model ................................ 4  \n2.2 Baseband equivalent model .......................... 5  \n2.2.1 Discrete-time baseband model for the channel ............ 7  \n2.3 OFDM .................................... 8  \n3 Machine Learning 11  \n3.1 Introduction ................................. 11  \n3.2 Perceptron ................................... 13  \n3.3 Support Vector Machine ........................... 14  \n3.3.1 Hard SVM .............................. 15  \n3.3.2 Soft SVM ............................... 16  \n3.4 Neural Networks ............................... 16  \n3.4.1 Convolutional Neural Networks ................... 18  \n3.5 Training, Validation and Test Set ....................... 20  \n4 Jamming detection with machine learning 23  \n4.1 Jamming detection on i-q diagrams ...................... 24  \n4.2 Jamming detection on waterfall plots ..................... 28  \n5 Experiments 31  \n5.1 Scenario .................................... 31  \n5.2 Setup and Data Acquisition .......................... 32  \n5.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34  \n5.3.1 I-Q Plots ............................... 35  \n5.3.2 Waterfall Plots ............................ 38  \n5.4 Data Analysis ................................. 40  \n5.4.1 I-Q Plots ............................... 40  \n5.4.2 Waterfall Plots ............................ 45  \n5.5 Results .................................... 47  \n5.5.1 I-Q Plots ............................... 47  \n5.5.2 Waterfall Plots ............................ 53  \n6 Conclusion and future works 61  \nReferences 65  \nAcknowledgments 67  \nListing of acronyms  \nSDR . . . . . . . . . . . Software Defined Radio IoT . . . . . . . . . . . . Internet of Things  \nAI . . . . . . . . . . . . . Artificial Intelligence  \nML . . . . . . . . . . . . Machine Learning  \nNN . . . . . . . . . . . . Neural Network  \nCNN . . . . . . . . . . Convolutional Neural Network AE . . . . . . . . . . . . Auto Encoder  \nSVM . . . . . . . . . . Support Vector Machine MSE . . . . . . . . . . . Mean Squared Error  \nCAE . . . . . . . . . . . Convolutional Auto Encoder DoS . . . . . . . . . . . Denial of Service  \nOFDM . . . . . . . . Orthogonal Frequency Division Multiplexing  \nSNR . . . . . . . . . . . Signal to Noise Ratio GPU . . . . . . . . . . . Graphical Processing Unit LTS . . . . . . . . . . . Long Term Support  \nAPI . . . . . . . . . . . Application Programming Interface CDF . . . . . . . . . . . Cumulative","cbCaimdpVpvvLJC1","https://ap.wps.com/l/cbCaimdpVpvvLJC1","pdf",2744205,1,77,"English","en",105,"# Abstract\n# Introduction\n# Cellular Communications\n## Channel model\n## Baseband equivalent model\n## OFDM\n# Machine Learning\n## Perceptron\n## Support Vector Machine\n## Neural Networks\n## Training, Validation and Test Set\n# Jamming detection with machine learning\n## Jamming detection on i-q diagrams\n## Jamming detection on waterfall plots\n# Experiments\n## Scenario\n## Setup and Data Acquisition\n## Dataset\n## Data Analysis\n## Results\n# Conclusion and future works\n# References","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"Detect whether a wireless cellular channel is being jammed by an attacker using machine learning applied to physical-layer observations.\"},{\"question\":\"How is the classification problem defined?\",\"answer\":\"The project builds a binary classifier that outputs whether the channel state is normal or corrupted by a jamming signal.\"},{\"question\":\"What hardware platform was used for the experiments?\",\"answer\":\"Experiments were carried out using Software Defined Radios, both on the user side and on the attacker side.\"}]","Physical Layer Jamming detection - a Machine Learning Approach | PDF",1785806834,194,{"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},"physical-layer-jamming-detection-a-machine-learning-approach","",{"@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/physical-layer-jamming-detection-a-machine-learning-approach/121787/",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","2026-08-04",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 main objective of the thesis?","Question",{"text":76,"@type":77},"Detect whether a wireless cellular channel is being jammed by an attacker using machine learning applied to physical-layer observations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the classification problem defined?",{"text":81,"@type":77},"The project builds a binary classifier that outputs whether the channel state is normal or corrupted by a jamming signal.",{"name":83,"@type":74,"acceptedAnswer":84},"What hardware platform was used for the experiments?",{"text":85,"@type":77},"Experiments were carried out using Software Defined Radios, both on the user side and on the attacker side.","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"]