[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121604-en":3,"doc-seo-121604-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":21,"html_lang":23,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121604,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Streamlined Intelligence - Resource-Efficient Machine Learning for 5G NR V2V Channel Equalization","Autonomous vehicles require reliable, efficient real-time communication to support safety and performance. This thesis investigates resource-efficient machine learning for 5G New Radio (NR) vehicle-to-vehicle (V2V) channel equalization, emphasizing lightweight random forest decision-tree models. Simulations use OFDM with QPSK in nonlinear channels including obstructions, Doppler shifts, and fading. Performance is assessed in MATLAB via BER, training time, and delay, compared against MMSE equalizers.","STREAMLINED INTELLIGENCE: RESOURCE-EFFICIENT MACHINE LEARNING FOR 5G NR V2V CHANNEL EQUALIZATION  \nA Thesis  \npresented to  \nthe Faculty of California Polytechnic State University, San Luis Obispo  \nIn Partial Fulfillment  \nof the Requirements for the Degree  \nMaster of Science in Electrical Engineering  \nby  \nJacqueline Radding  \nJune 2025  \n© 2025  \nJacqueline Radding ALL RIGHTS RESERVED  \nii  \nCOMMITTEE MEMBERSHIP  \nTITLE: Streamlined Intelligence: Resource-Efficient Machine Learning for 5G NR V2V Channel Equalization  \nAUTHOR: Jacqueline Radding  \nDATE SUBMITTED: June 2025  \nCOMMITTEE CHAIR: Payam Nayeri, PhD.  \nAssistant Professor of Electrical Engineering  \nCOMMITTEE MEMBER: Clay McKell, PhD.  \nLecturer in Electrical Engineering  \nCOMMITTEE MEMBER: Siavash Farzan, PhD.  \nAssistant Professor of Electrical Engineering  \nABSTRACT  \nStreamlined Intelligence: Resource-Efficient Machine Learning for 5G NR V2V  \nChannel Equalization  \nJacqueline Radding  \nAs autonomous vehicles continue to evolve, reliable and efficient real-time communication between vehicles is essential for safety and performance. This thesis explores Streamlined Intelligence: Resource Efficient machine learning for 5G NR V2V Channel Equalization, focusing on lightweight random forest decision tree models to address the challenges of channel equalization in 5G New Radio (NR) vehicle to vehicle (V2V) systems. Using orthogonal frequency division multiplexing (OFDM) with QPSK modulation, the study simulates data transmission in nonlinear channels characterized by obstructions, Doppler shifts, and fading. Decision trees are proposed asa computationally efficient alternative to other machine learning methods while being compared to traditional methods such as minimum mean square error (MMSE) equalizers. Using MATLAB, the performance of these models is evaluated on the basis of bit error rate (BER), training time, and delay under varying channel conditions. The random forest equalizer BER outperforms MMSE in non-linear channel conditions. However, the random forest adds prediction time to the system and a vast amount of training time. This work aims to demonstrate the potential of resource-efficient machine learning in achieving high-performance channel equalization, paving the way for scalable and effective communication in next-generation autonomous systems.  \nKeywords: Digital Communications, 5G, New Radio, Machine Learning, Decision Tree.  \nACKNOWLEDGMENTS  \nThanks to:  \n• My family, friends, and professors for their support.  \n• Professor Nayeri for his guidance.  \n• The Cal Poly IEEE club for its community and support in my electrical engineering endeavors.  \nTABLE OF CONTENTS  \nPage  \nLIST OF TABLES ................................. viii  \nLIST OF FIGURES ................................ ix  \nCHAPTER  \n1. INTRODUCTION ............................... 1  \n1.1 Problem Statement ............................ 1  \n1.2 Big Picture Goals ............................. 2  \n1.3 Decision Trees ............................... 3  \n1.3.1 Decision Tree Challenges .................... 4  \n1.3.2 Decision Tree Impact and Significance ............. 6  \n1.4 Thesis Outline ............................... 7  \n2. BACKGROUND ................................ 9  \n2.1 OFDM ................................... 9  \n2.1.1 QPSK and QAM Modulation .................. 11  \n2.2 Communication Technologies ....................... 14  \n2.2.1 C-V2X(Cellular Vehicle to Everything) ............. 15  \n2.2.2 IEEE 802.11p ........................... 17  \n2.3 5G NR ................................... 18  \n2.3.1 3GPP ............................... 23  \n2.4 Channel Modeling ............................. 24  \n2.5 Building 5G NR Channel Models .................... 27  \n2.6 Channel Estimation ........................... 30  \n2.7 Traditional Channel Equalization .................... 31  \n2.8 Measuring the impacts of signal noise ................. 32  \n3. DECISION TREE EQUALIZER SYSTEM .................. 35  \n","cbCaiaYEpRBYLmvX","https://ap.wps.com/l/cbCaiaYEpRBYLmvX","pdf",3435425,1,105,"English","en","# Introduction\n## Problem Statement\n## Big Picture Goals\n## Decision Trees\n## Thesis Outline\n# Background\n## OFDM\n## Communication Technologies\n## 5G NR\n## Channel Modeling\n## Channel Estimation\n## Traditional Channel Equalization\n## Measuring the impacts of signal noise\n# Decision Tree Equalizer System\n## Machine Learning\n## Decision Tree Regression Channel Equalization\n## Random Forest Decision Trees\n## Previous Works\n# Decision Tree Equalizer Performance\n## Decision Tree Training\n## AWGN Case Performance\n## Varying Channel Factors Performance\n# Conclusion\n## Outlook\n## Future Work","[{\"question\":\"What problem does the thesis address in 5G NR V2V systems?\",\"answer\":\"It targets channel equalization challenges in 5G NR V2V communication that arise under nonlinear conditions such as obstructions, Doppler shifts, and fading.\"},{\"question\":\"Which machine learning approach is proposed for equalization?\",\"answer\":\"The thesis proposes lightweight random forest decision-tree equalizer models as a computationally efficient alternative to other machine learning methods.\"},{\"question\":\"How is model performance evaluated and what is the main trade-off?\",\"answer\":\"Models are evaluated in MATLAB using BER, training time, and delay, compared with traditional MMSE equalizers; random forest improves BER in nonlinear channels but increases prediction time and requires substantial training time.\"}]","Streamlined Intelligence - Resource-Efficient Machine Learning for 5G NR V2V Channel Equalization | PDF",1785736440,265,{"code":4,"msg":30,"data":31},"ok",{"site_id":21,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"streamlined-intelligence-resource-efficient-machine-learning-for-5g-nr-v2v-channel-equalization","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/streamlined-intelligence-resource-efficient-machine-learning-for-5g-nr-v2v-channel-equalization/121604/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the thesis address in 5G NR V2V systems?","Question",{"text":74,"@type":75},"It targets channel equalization challenges in 5G NR V2V communication that arise under nonlinear conditions such as obstructions, Doppler shifts, and fading.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning approach is proposed for equalization?",{"text":79,"@type":75},"The thesis proposes lightweight random forest decision-tree equalizer models as a computationally efficient alternative to other machine learning methods.",{"name":81,"@type":72,"acceptedAnswer":82},"How is model performance evaluated and what is the main trade-off?",{"text":83,"@type":75},"Models are evaluated in MATLAB using BER, training time, and delay, compared with traditional MMSE equalizers; 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