[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118092-en":3,"doc-seo-118092-105":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":4,"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},118092,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Prediction of Power to Autonomous Vehicles using Machine Learning techniques","Machine learning techniques are applied to intelligent transport systems for connected and automated vehicles, targeting downlink communications between a single-antenna base transceiver station and autonomous vehicles transmitting at varying power levels. The study predicts optimal transmit power under diverse channel conditions to reduce interference and improve link performance. LSTM and feedforward neural networks are evaluated for transmit-power optimization, with comparative results reported using mean square error metrics, supporting ML-assisted autonomous driving communication reliability and efficiency.","This is a repository copy of Allocation of Power to Autonomous Vehicles Using Machine Learning.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/213988/](https://eprints.whiterose.ac.uk/213988/)  \n[Version: Accepted Version](Version: Accepted Version)  \nProceedings Paper:  \nAlruwaili, M.A.H.A., Djemame, K. and Zhang, [L. orcid.org/0000-0002-4535-3200](L. orcid.org/0000-0002-4535-3200)  \n(Accepted: 2024) Allocation of Power to Autonomous Vehicles Using Machine Learning. In: Proceedings of The 11th International Conference on Wireless Networks and Mobile Communications. The 11th International Conference on Wireless Networks and Mobile Communications, 23-25 Jul 2024, Leeds. IEEE (In Press)  \n© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, forresale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  \nReuse  \nItems deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item.  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nPrediction of Power to Autonomous Vehicles using  \nMachine Learning techniques  \nMaha Alruwail  \nSchool of Computing, University of Leeds Northern Border University, KSA  \nEmail: [elmmha@leeds.ac.uk](elmmha@leeds.ac.uk)  \nKarim Djemame,  \nSchool of Computing University of Leeds Leeds, United Kingdom  \nEmail: [k.djemame@leeds.ac.uk](k.djemame@leeds.ac.uk)  \nLi Zhang,  \nSchool of Electronic and Electrical Engineering, University of Leeds Leeds, United Kingdom [Email:l.x.zhang@leeds.ac.uk](Email:l.x.zhang@leeds.ac.uk)  \nAbstract—The integration of machine learning (ML) techniques has catalyzed signi􀀂cant advancements in the realm of autonomous vehicle technology, particularly in the domain of Intelligent Transport Systems (ITS) and the evolution of Connected and Automated Vehicles (CAVs). This study focuses on a downlink communication network characterized by a single-antenna Base Transceiver Station (BTS) and autonomous vehicles, with the BTS transmitting information at varying power levels. The primary objective is to predict optimal transmit power for vehicles across diverse channel conditions using machine learning methodologies, aimed at mitigating interference within the system. This interdisciplinary research endeavors to optimize transmit power from the BTS to vehicles through the synergy of machine learning and optimization techniques. By addressing this imperative, we aim to enhance vehicle safety, ef􀀂ciency, and reliability within modern transportation networks. Leveraging advanced ML models, including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FNN), our investigation reveals promising insights into the ef􀀂cacy of these algorithms in advancing autonomous driving technologies. The paper presents comparative analyses of two prominent machine learning models, with the Mean Square Error (MSE) computed at 17.2516 for LSTM and 13.8562 for the Feedforward Model. These results underscore the potential of ML-driven approaches in optimizing transmit power for autonomous vehic","cbCaiu4Rs5FFVlk9","https://ap.wps.com/l/cbCaiu4Rs5FFVlk9","pdf",436317,1,7,"English","en",105,"# Introduction\n## Downlink communication network and power-level variability\n## Role of autonomous driving and connected automated vehicles\n## Motivation: interference mitigation and transport safety","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It predicts optimal downlink transmit power for autonomous vehicles under different channel conditions to mitigate interference in the communication system.\"},{\"question\":\"Which machine learning models are used for power prediction?\",\"answer\":\"The study evaluates Long Short-Term Memory (LSTM) and a Feedforward Neural Network (FNN) model for transmit power prediction.\"},{\"question\":\"How is the model performance evaluated?\",\"answer\":\"Performance is compared using Mean Square Error (MSE), with reported values for the LSTM and feedforward approaches.\"}]","Prediction of Power to Autonomous Vehicles using Machine Learning techniques | PDF",1785681492,18,{"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},"prediction-of-power-to-autonomous-vehicles-using-machine-learning-techniques","",{"@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/prediction-of-power-to-autonomous-vehicles-using-machine-learning-techniques/118092/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"It predicts optimal downlink transmit power for autonomous vehicles under different channel conditions to mitigate interference in the communication system.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used for power prediction?",{"text":80,"@type":76},"The study evaluates Long Short-Term Memory (LSTM) and a Feedforward Neural Network (FNN) model for transmit power prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model performance evaluated?",{"text":84,"@type":76},"Performance is compared using Mean Square Error (MSE), with reported values for the LSTM and feedforward approaches.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]