{"id":"https://openalex.org/W3162490036","doi":"https://doi.org/10.1109/wcnc49053.2021.9417509","title":"Recurrent Neural Network Based Beam Prediction for Millimeter-Wave 5G Systems","display_name":"Recurrent Neural Network Based Beam Prediction for Millimeter-Wave 5G Systems","publication_year":2021,"publication_date":"2021-03-29","ids":{"openalex":"https://openalex.org/W3162490036","doi":"https://doi.org/10.1109/wcnc49053.2021.9417509","mag":"3162490036"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc49053.2021.9417509","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc49053.2021.9417509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-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/A5054602675","display_name":"Shubham Khunteta","orcid":"https://orcid.org/0000-0001-8188-7851"},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Shubham Khunteta","raw_affiliation_strings":["Modem Advanced Team, Communication R&D Samsung R&D Institute, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Modem Advanced Team, Communication R&D Samsung R&D Institute, Bengaluru, India","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020718837","display_name":"Ashok Kumar Reddy Chavva","orcid":"https://orcid.org/0000-0002-0772-1631"},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashok Kumar Reddy Chavva","raw_affiliation_strings":["Modem Advanced Team, Communication R&D Samsung R&D Institute, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Modem Advanced Team, Communication R&D Samsung R&D Institute, Bengaluru, India","institution_ids":["https://openalex.org/I4210139030"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5054602675"],"corresponding_institution_ids":["https://openalex.org/I4210139030"],"apc_list":null,"apc_paid":null,"fwci":0.3008,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.549527,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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/T10262","display_name":"Microwave Engineering and Waveguides","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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9897000193595886,"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/computer-science","display_name":"Computer science","score":0.6222921013832092},{"id":"https://openalex.org/keywords/path-loss","display_name":"Path loss","score":0.5612362027168274},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.4963131546974182},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.48820239305496216},{"id":"https://openalex.org/keywords/beam","display_name":"Beam (structure)","score":0.4740312397480011},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4658805727958679},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4382787048816681},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.40889066457748413},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.3408544957637787},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.27338945865631104},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.20182523131370544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.168588787317276},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13189876079559326},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.09988319873809814},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.08015462756156921}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6222921013832092},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.5612362027168274},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.4963131546974182},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.48820239305496216},{"id":"https://openalex.org/C168834538","wikidata":"https://www.wikidata.org/wiki/Q3705329","display_name":"Beam (structure)","level":2,"score":0.4740312397480011},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4658805727958679},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4382787048816681},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.40889066457748413},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.3408544957637787},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.27338945865631104},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.20182523131370544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.168588787317276},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13189876079559326},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.09988319873809814},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.08015462756156921}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc49053.2021.9417509","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc49053.2021.9417509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2555593130","https://openalex.org/W2751147412","https://openalex.org/W2796047239","https://openalex.org/W2898434483","https://openalex.org/W2914111477","https://openalex.org/W2963742775","https://openalex.org/W2997530752","https://openalex.org/W3010477238","https://openalex.org/W3094706131","https://openalex.org/W3098782124","https://openalex.org/W3105281589"],"related_works":["https://openalex.org/W1588057903","https://openalex.org/W2953613577","https://openalex.org/W2785107490","https://openalex.org/W3110590130","https://openalex.org/W4287562279","https://openalex.org/W2504274537","https://openalex.org/W905396345","https://openalex.org/W2972536919","https://openalex.org/W2512234407","https://openalex.org/W2521006054"],"abstract_inverted_index":{"5G":[0],"millimeter-wave":[1],"(mmWave)":[2],"system":[3,119],"provides":[4],"ultra":[5],"low":[6],"latency":[7],"and":[8,32,92,113,143,223],"higher":[9,18],"peak":[10],"data":[11,49,90,142],"rate":[12,91,204],"with":[13,140,156,189],"a":[14,124,157],"major":[15],"drawback":[16],"of":[17,66,106,115,152,163,205,220],"path":[19,39],"loss":[20],"at":[21,28,118,202],"mmWave":[22],"spectrum.":[23],"Multiple":[24],"beams":[25,218],"are":[26,53],"formed":[27],"base":[29],"station":[30],"(BS)":[31],"user":[33],"equipment":[34],"(UE)":[35],"to":[36,126,233],"compensate":[37],"excessive":[38],"loss.":[40],"To":[41],"help":[42],"find":[43],"the":[44,64,80,100,131,150,153,161,165,179,183,203,221,226],"best":[45,81,132,166,210],"beam":[46,51,68,82,107,133,144,167,212],"pair":[47,69,134,168],"for":[48,63,130,186,195],"transmission,":[50],"measurements":[52,145],"performed":[54],"continuously,":[55],"typically":[56],"in":[57,76,78,85,88,170,178,214,225],"round":[58],"robin":[59],"fashion.":[60],"Time":[61],"taken":[62],"measurement":[65,187],"full":[67],"set":[70],"can":[71],"be":[72],"large":[73],"which":[74,84],"results":[75,87],"delay":[77],"finding":[79],"pair,":[83],"turn":[86],"poor":[89],"link":[93],"quality.":[94],"In":[95],"this":[96],"paper,":[97],"we":[98,122,175],"analyse":[99],"key":[101],"factors":[102],"affecting":[103],"signal":[104],"strength":[105],"pairs":[108],"such":[109],"as":[110,146],"device":[111],"orientation":[112,200],"angle":[114],"arrival":[116],"(AoA)":[117],"level.":[120],"Further,":[121,174],"propose":[123],"method":[125,155],"predict":[127],"top-K":[128,171],"candidates":[129,185],"using":[135],"recurrent":[136],"neural":[137],"networks":[138],"(RNN)":[139],"sensor":[141],"inputs.":[147],"We":[148,192],"evaluate":[149],"performance":[151,158],"proposed":[154],"metric":[159],"showing":[160],"number":[162],"times":[164,222],"is":[169,213,228],"predicted":[172,184,216],"candidates.":[173],"show":[176,193],"gain":[177,224],"throughput":[180,227],"by":[181],"scheduling":[182],"compared":[188,232],"conventional":[190,234],"scheduling.":[191],"that":[194],"an":[196],"UE":[197,211,217],"changing":[198],"its":[199],"even":[201],"90":[206],"degree":[207],"per":[208],"second,":[209],"Top-5":[215],"99%":[219],"more":[229],"than":[230],"50%":[231],"methods.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
