[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126972-en":3,"doc-seo-126972-105":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},126972,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Beam Training in mmWave Vehicular Systems - Machine Learning for Decoupling Beam Selection","Codebook-based beam selection configures millimeter-wave links by testing transmit and receive beamforming codeword pairs, but frequent reconfiguration overhead is costly in highly dynamic vehicular settings. This work develops location-aided machine-learning approaches that decouple beam selection decisions between user equipment (UE) and the base station (BS). Performance gaps from decoupling and from disaggregating UE location information are quantified through simulations. Results show BS-side decoupled selection with available location information matches joint BS beam pair selection, while decoupled selection without location approaches it when enough beam pairs are swept.","Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection  \nIbrahim Kilinc∗ , Ryan M. Dreifuerst†, Junghoon Kim‡, and Robert W. Heath Jr.∗  \n∗ ECE Department, University of California, San Diego, USA (e-mail: {ikilinc, [rwheathjr](rwheathjr}@ucsd.edu)[}](rwheathjr}@ucsd.edu)[@ucsd.edu](rwheathjr}@ucsd.edu))  \n†ECE Department, NC State University, Raleigh, USA (e-mail: [rmdreifu@ncsu.edu](rmdreifu@ncsu.edu))‡Motorola Mobility, Chicago, IL 60654, USA (email: [junghoon@motorola.com](junghoon@motorola.com))  \narXiv :2404 . 10936v1 [ ee ss . SP] 16 Apr 2024  \nAbstract—Codebook-based beam selection is one approach for configuring millimeter wave communication links. The overhead required to reconfigure the transmit and receive beam pair, though, increases in highly dynamic vehicular communication systems. Location information coupled with machine learning (ML) beam recommendation is one way to reduce the overhead of beam pair selection. In this paper, we develop ML-based locationaided approaches to decouple the beam selection between the user equipment (UE) and the base station (BS). We quantify the performance gaps due to decoupling beam selection and also disaggregating the UE’s location information from the BS. Our simulation results show that decoupling beam selection with available location information at the BS performs comparable to joint beam pair selection at the BS. Moreover, decoupled beam selection without location closely approaches the performance of beam pair selection at the BS when sufficient beam pairs are swept.  \nI. INTRODUCTION  \nMillimeter wave (mmWave) multiple-input multiple-output (MIMO) communications promise enhanced connectivity with high-fidelity sensor data exchange in vehicular systems [1] . Obtaining the best performance in mmWave MIMO systems requires configuring transmit and receive antenna arrays, which is challenging with large arrays and hybrid architectures [2],[3] . Codebook-based beam training is one approach for configuring mmWave MIMO links by transmitting and receiving with each beamforming codeword-pair in highly dynamic vehicular environments [2], [3] . Prior work has demonstrated how sensory information from localization sensors [4]–[6], camera [5], LiDAR [6], radar [7] can be leveraged to reduce the overheads of beam training. Most prior work on ML-based beam training focuses on a centralized approach where the BS leverages sensor data to recommend beam pairs to test [4]–[7] . This requires the recommended receive beams to be shared with the UEs and requires all UEs to have the same codebook and antenna configuration.  \nIn this work, we develop location-aided beam training approaches that decouple the beam selection at the BS and the UE. We consider three scenarios. Scenario 1 represents our baseline case where the BS determines the beam pairs for the BS and the UE based on the UE’s location information. In the scenario 2, the BS selects its transmit beams based on the UE location. Independently, the UE determines its receive beams by leveraging its location. In the scenario 3, the BS does not have the information of the UE’s location and the selected  \ntransmit beams are chosen to serve the region of interest in the urban street. The UE selects its beam based on its location information. We denote beam selection in the scenario 1 as coupled with location, and beam selection in the scenario 2 and 3 as decoupled with and without location. We develop MLbased beam selection algorithms for coupled and decoupled scenarios because ML has been shown to successfully learn implicit relationships between beams and location information in site-specific scenarios [8] . The algorithms for scenario 2 and 3 are based on lightweight ML models for practical deployment at the UE. We generate ray-traced channel samples in a realistic urban environment. Accordingly, we compare the three scenarios to quantify the performance gaps due to decoupling beam selection and disaggreg","cbCaicYQlIQje9Px","https://ap.wps.com/l/cbCaicYQlIQje9Px","pdf",1088884,1,6,"English","en",105,"# Introduction\n## Prior work and motivation\n## Proposed scenarios and decoupling approach\n# System Model and Problem Formulation\n## System model\n## General beam training problem\n## Location-based dataset","[{\"question\":\"Why does codebook-based beam training create overhead in vehicular mmWave systems?\",\"answer\":\"Reconfiguration of the transmit and receive beam pair is required in dynamic scenarios, increasing the overhead during beam training.\"},{\"question\":\"What does the paper mean by decoupling beam selection between the UE and the BS?\",\"answer\":\"Instead of joint selection, beam decisions are split across UE and BS so that transmit and receive beam choices are made using separate information and possibly different location availability.\"},{\"question\":\"How many scenarios are considered and what key difference distinguishes them?\",\"answer\":\"Three scenarios are considered. Scenario 1 uses coupled selection with UE location for both BS and UE, while scenarios 2 and 3 use decoupled selection, with scenario 2 using UE location at BS and scenario 3 lacking UE location at the BS.\"}]","Beam Training in mmWave Vehicular Systems - Machine Learning for Decoupling Beam Selection | PDF",1785935976,15,{"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},"beam-training-in-mmwave-vehicular-systems-machine-learning-for-decoupling-beam-selection","",{"@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/beam-training-in-mmwave-vehicular-systems-machine-learning-for-decoupling-beam-selection/126972/",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","2026-08-22","2026-08-05",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},"Why does codebook-based beam training create overhead in vehicular mmWave systems?","Question",{"text":76,"@type":77},"Reconfiguration of the transmit and receive beam pair is required in dynamic scenarios, increasing the overhead during beam training.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What does the paper mean by decoupling beam selection between the UE and the BS?",{"text":81,"@type":77},"Instead of joint selection, beam decisions are split across UE and BS so that transmit and receive beam choices are made using separate information and possibly different location availability.",{"name":83,"@type":74,"acceptedAnswer":84},"How many scenarios are considered and what key difference distinguishes them?",{"text":85,"@type":77},"Three scenarios are considered. Scenario 1 uses coupled selection with UE location for both BS and UE, while scenarios 2 and 3 use decoupled selection, with scenario 2 using UE location at BS and scenario 3 lacking UE location at the BS.","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},[94,98,102,106,111,115,120,123,128,131,135],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]