{"id":"https://openalex.org/W4386609098","doi":"https://doi.org/10.1109/tvt.2023.3313548","title":"A Novel mmWave Beam Alignment Approach for Beyond 5G Autonomous Vehicle Networks","display_name":"A Novel mmWave Beam Alignment Approach for Beyond 5G Autonomous Vehicle Networks","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386609098","doi":"https://doi.org/10.1109/tvt.2023.3313548"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2023.3313548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2023.3313548","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071190727","display_name":"Rima Benelmir","orcid":"https://orcid.org/0000-0003-2335-5958"},"institutions":[{"id":"https://openalex.org/I206961696","display_name":"University of Biskra","ror":"https://ror.org/05fr5y859","country_code":"DZ","type":"education","lineage":["https://openalex.org/I206961696"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Rima Benelmir","raw_affiliation_strings":["LESIA Laboratory, Department of Computer Science, Mohamed Khider University of Biskra, Biskra, Algeria"],"raw_orcid":"https://orcid.org/0000-0003-2335-5958","affiliations":[{"raw_affiliation_string":"LESIA Laboratory, Department of Computer Science, Mohamed Khider University of Biskra, Biskra, Algeria","institution_ids":["https://openalex.org/I206961696"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048857332","display_name":"Salim Bitam","orcid":"https://orcid.org/0000-0001-5618-4126"},"institutions":[{"id":"https://openalex.org/I206961696","display_name":"University of Biskra","ror":"https://ror.org/05fr5y859","country_code":"DZ","type":"education","lineage":["https://openalex.org/I206961696"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Salim Bitam","raw_affiliation_strings":["LESIA Laboratory, Department of Computer Science, University of Biskra, Biskra, Algeria"],"raw_orcid":"https://orcid.org/0000-0001-5618-4126","affiliations":[{"raw_affiliation_string":"LESIA Laboratory, Department of Computer Science, University of Biskra, Biskra, Algeria","institution_ids":["https://openalex.org/I206961696"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077888313","display_name":"Scott Fowler","orcid":"https://orcid.org/0000-0002-0019-8411"},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Scott Fowler","raw_affiliation_strings":["Department of Science and Technology, Linköping University, Norrköping, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-0019-8411","affiliations":[{"raw_affiliation_string":"Department of Science and Technology, Linköping University, Norrköping, Sweden","institution_ids":["https://openalex.org/I102134673"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045421775","display_name":"Abdelhamid Mellouk","orcid":"https://orcid.org/0000-0002-6886-7394"},"institutions":[{"id":"https://openalex.org/I197681013","display_name":"Universit\u00e9 Paris-Est Cr\u00e9teil","ror":"https://ror.org/05ggc9x40","country_code":"FR","type":"education","lineage":["https://openalex.org/I197681013"]},{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Abdelhamid Mellouk","raw_affiliation_strings":["TincNET(CIR).LISSI Research Team, Department of Networks and Telecommunication, University of Paris-Est Créteil (UPEC), Vitry-sur-Seine, France"],"raw_orcid":"https://orcid.org/0000-0002-6886-7394","affiliations":[{"raw_affiliation_string":"TincNET(CIR).LISSI Research Team, Department of Networks and Telecommunication, University of Paris-Est Créteil (UPEC), Vitry-sur-Seine, France","institution_ids":["https://openalex.org/I2800365227","https://openalex.org/I197681013"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5953,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.83518198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"73","issue":"2","first_page":"1597","last_page":"1610"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.8577451705932617},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6150241494178772},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5824993848800659},{"id":"https://openalex.org/keywords/base-station","display_name":"Base station","score":0.5265879034996033},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45842257142066956},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.4486734867095947},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.4317009449005127},{"id":"https://openalex.org/keywords/beam","display_name":"Beam (structure)","score":0.4300433397293091},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39912140369415283},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.35315126180648804},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2434270679950714},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.202504962682724},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15600454807281494},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14102321863174438}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.8577451705932617},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6150241494178772},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5824993848800659},{"id":"https://openalex.org/C68649174","wikidata":"https://www.wikidata.org/wiki/Q1379116","display_name":"Base station","level":2,"score":0.5265879034996033},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45842257142066956},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.4486734867095947},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.4317009449005127},{"id":"https://openalex.org/C168834538","wikidata":"https://www.wikidata.org/wiki/Q3705329","display_name":"Beam (structure)","level":2,"score":0.4300433397293091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39912140369415283},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.35315126180648804},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2434270679950714},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.202504962682724},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15600454807281494},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14102321863174438},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2023.3313548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2023.3313548","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W2004294022","https://openalex.org/W2111953900","https://openalex.org/W2614851539","https://openalex.org/W2615358882","https://openalex.org/W2749772809","https://openalex.org/W2756203131","https://openalex.org/W2765421612","https://openalex.org/W2790223875","https://openalex.org/W2793446253","https://openalex.org/W2796456238","https://openalex.org/W2805089611","https://openalex.org/W2892342145","https://openalex.org/W2898434483","https://openalex.org/W2905258433","https://openalex.org/W2906979799","https://openalex.org/W2913291195","https://openalex.org/W2942119937","https://openalex.org/W2951145066","https://openalex.org/W2962691384","https://openalex.org/W2963487795","https://openalex.org/W2972072389","https://openalex.org/W3008638138","https://openalex.org/W3035352987","https://openalex.org/W3043284621","https://openalex.org/W3045822555","https://openalex.org/W3100886385","https://openalex.org/W3102511045","https://openalex.org/W3103720336","https://openalex.org/W3135242915","https://openalex.org/W3163731022","https://openalex.org/W3173500807","https://openalex.org/W3193816080","https://openalex.org/W3200993033","https://openalex.org/W4226110397","https://openalex.org/W4226381701","https://openalex.org/W4280581140","https://openalex.org/W4288574671","https://openalex.org/W4321374739","https://openalex.org/W4322755838","https://openalex.org/W6759127422","https://openalex.org/W6801628547"],"related_works":["https://openalex.org/W2303288554","https://openalex.org/W3033750547","https://openalex.org/W2029216794","https://openalex.org/W4386698331","https://openalex.org/W2047225036","https://openalex.org/W2138314731","https://openalex.org/W2029263917","https://openalex.org/W2093207996","https://openalex.org/W2982446297","https://openalex.org/W2322468729"],"abstract_inverted_index":{"Nowadays,":[0],"directional":[1],"communication":[2,11,44],"represents":[3],"a":[4,35,41,76,121],"high":[5,26],"potential":[6],"solution":[7],"for":[8,141,198],"Vehicle-to-Everything":[9],"(V2X)":[10],"in":[12,28,73],"Beyond-Fifth-Generation":[13],"(B5G)":[14],"vehicular":[15,30],"networks,":[16],"using":[17,165],"the":[18,25,29,54,58,62,83,91,101,111,146,151],"millimeter":[19],"wave":[20],"(mmWave)":[21],"technique.":[22],"Due":[23],"to":[24,33,39,52,81,97],"mobility":[27],"environment":[31],"and":[32,61,127,150,179,196,201,215],"ensure":[34],"higher":[36],"data":[37,99],"rate":[38],"establish":[40],"reliable":[42],"V2X":[43],"link,":[45],"an":[46,114],"efficient":[47],"beam":[48,78,85,117],"alignment":[49,79,118],"is":[50,138],"needed":[51],"point":[53],"optimal":[55,84],"direction":[56],"between":[57],"moving":[59,102],"vehicle":[60,103,143],"mmWave":[63,92],"base":[64,93],"station.":[65],"To":[66],"deal":[67],"with":[68,145],"this":[69,74],"issue,":[70],"we":[71],"propose":[72],"paper":[75],"new":[77],"method":[80],"select":[82],"angle":[86,137],"of":[87,107,113,158,191],"Departure":[88],"(AoD)":[89],"from":[90],"station,":[94],"used":[95],"further":[96],"transmit":[98],"toward":[100],"directly.":[104],"The":[105,135,156,183],"novelty":[106],"our":[108,159,187],"proposal":[109],"concerns":[110],"suggestion":[112],"original":[115],"hybrid":[116],"approach,":[119],"combining":[120],"128-filters-based":[122],"Convolutional":[123],"Neural":[124],"Network":[125],"(CNN)":[126],"4-layers":[128],"based":[129],"Bidirectional":[130],"Long":[131],"Short-Term":[132],"Memory":[133],"(BiLSTM).":[134],"selected":[136],"performed":[139],"automatically":[140],"each":[142],"position":[144],"lowest":[147],"error":[148,173,177,181],"probability":[149],"highest":[152],"received":[153],"signal":[154],"power.":[155],"performance":[157],"proposed":[160],"CNN-BiLSTM":[161],"model":[162,188],"was":[163],"evaluated":[164],"popular":[166],"regression":[167],"metrics":[168],"such":[169,209],"as":[170,210],"mean":[171,175],"squared":[172],"(MSE),":[174],"absolute":[176],"(MAE),":[178],"root-mean-square":[180],"(RMSE).":[182],"results":[184],"show":[185],"that":[186],"achieved":[189],"values":[190],"at":[192],"least":[193],"0.0107,":[194],"0.0765,":[195],"0.103":[197],"MSE,":[199],"MAE,":[200],"RMSE,":[202],"respectively,":[203],"outperforming":[204],"other":[205],"machine":[206],"learning":[207],"algorithms":[208],"KNN":[211],"regressor,":[212],"SVR,":[213],"BiLSTM,":[214],"CNN-LSTM.":[216]},"counts_by_year":[{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}