{"id":"https://openalex.org/W2947279656","doi":"https://doi.org/10.1109/ieeeconf44664.2019.9048966","title":"Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems","display_name":"Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W2947279656","doi":"https://doi.org/10.1109/ieeeconf44664.2019.9048966","mag":"2947279656"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf44664.2019.9048966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf44664.2019.9048966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1905.13212","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100347674","display_name":"Xiaofeng Li","orcid":"https://orcid.org/0000-0001-7195-9321"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaofeng Li","raw_affiliation_strings":["Arizona State University","Arizona State University**"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Arizona State University**","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003243464","display_name":"Ahmed Alkhateeb","orcid":"https://orcid.org/0000-0001-5648-1569"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed Alkhateeb","raw_affiliation_strings":["Arizona State University","Arizona State University**"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Arizona State University**","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100347674"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":1.9363,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.86438438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"800","last_page":"805"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":1.0,"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":1.0,"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":0.9991999864578247,"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":0.9980999827384949,"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/precoding","display_name":"Precoding","score":0.8181488513946533},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7390105724334717},{"id":"https://openalex.org/keywords/beamforming","display_name":"Beamforming","score":0.7187362909317017},{"id":"https://openalex.org/keywords/mimo","display_name":"MIMO","score":0.674935519695282},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6506150960922241},{"id":"https://openalex.org/keywords/transmitter","display_name":"Transmitter","score":0.6017261147499084},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.594577431678772},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49757102131843567},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49221816658973694},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48613688349723816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4732138216495514},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.4414486587047577},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.4172448217868805},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.39627805352211},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.35742753744125366},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3315774202346802},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.19891667366027832},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1842820644378662}],"concepts":[{"id":"https://openalex.org/C160562895","wikidata":"https://www.wikidata.org/wiki/Q7239557","display_name":"Precoding","level":4,"score":0.8181488513946533},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7390105724334717},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.7187362909317017},{"id":"https://openalex.org/C207987634","wikidata":"https://www.wikidata.org/wiki/Q176862","display_name":"MIMO","level":3,"score":0.674935519695282},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6506150960922241},{"id":"https://openalex.org/C47798520","wikidata":"https://www.wikidata.org/wiki/Q190157","display_name":"Transmitter","level":3,"score":0.6017261147499084},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.594577431678772},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49757102131843567},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49221816658973694},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48613688349723816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4732138216495514},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.4414486587047577},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.4172448217868805},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.39627805352211},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.35742753744125366},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3315774202346802},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.19891667366027832},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1842820644378662},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ieeeconf44664.2019.9048966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf44664.2019.9048966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1905.13212","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.13212","pdf_url":"https://arxiv.org/pdf/1905.13212","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2947279656","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1905.13212","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1905.13212","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1905.13212","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1905.13212","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.13212","pdf_url":"https://arxiv.org/pdf/1905.13212","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2947279656.pdf","grobid_xml":"https://content.openalex.org/works/W2947279656.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1483249443","https://openalex.org/W2051388965","https://openalex.org/W2053521124","https://openalex.org/W2056798980","https://openalex.org/W2075148031","https://openalex.org/W2092397817","https://openalex.org/W2111953900","https://openalex.org/W2116334496","https://openalex.org/W2120645748","https://openalex.org/W2189661557","https://openalex.org/W2195693430","https://openalex.org/W2254715784","https://openalex.org/W2618946976","https://openalex.org/W2624871570","https://openalex.org/W2885140458","https://openalex.org/W2914949576","https://openalex.org/W2963408914","https://openalex.org/W3013594094","https://openalex.org/W3134059753","https://openalex.org/W6738884980","https://openalex.org/W6739365718","https://openalex.org/W6759127422","https://openalex.org/W6762532188","https://openalex.org/W6763068224"],"related_works":["https://openalex.org/W3203938711","https://openalex.org/W3125412527","https://openalex.org/W2195693430","https://openalex.org/W3214234391","https://openalex.org/W2981319699","https://openalex.org/W3165862203","https://openalex.org/W3180149110","https://openalex.org/W2611352293","https://openalex.org/W3110673546","https://openalex.org/W2915324925","https://openalex.org/W2956320240","https://openalex.org/W3010766834","https://openalex.org/W2958808855","https://openalex.org/W3188212407","https://openalex.org/W3125134142","https://openalex.org/W3195605735","https://openalex.org/W3048896094","https://openalex.org/W3013594094","https://openalex.org/W2963408914","https://openalex.org/W3167193327"],"abstract_inverted_index":{"This":[0,13,81],"paper":[1],"proposes":[2],"a":[3,31,85,157],"novel":[4,86],"neural":[5,87],"network":[6,15,88],"architecture,":[7],"that":[8,90,138],"we":[9],"call":[10],"an":[11,67],"auto-precoder.":[12],"auto-precoder":[14],"jointly":[16],"senses":[17],"the":[18,25,37,43,47,56,63,72,76,93,96,101,111,118,123,128,139,145,173,187,192],"millimeter":[19],"wave":[20],"(mmWave)":[21],"channel":[22,48,58,130,181],"and":[23,99,162,195],"designs":[24],"hybrid":[26,124,193],"precoding":[27],"matrices":[28,104],"with":[29,166],"only":[30,177],"few":[32],"training":[33,146,182],"pilots.":[34],"More":[35],"specifically,":[36],"proposed":[38,112,140,174],"machine":[39],"learning":[40],"model":[41,113],"leverages":[42],"prior":[44],"observations":[45],"of":[46,122,159,191],"to":[49,70,116,149,184],"achieve":[50,196],"two":[51,106],"objectives.":[52],"First,":[53],"it":[54],"optimizes":[55],"compressive":[57],"sensing":[59,73],"vectors":[60,121,190],"based":[61],"on":[62,75,95],"surrounding":[64],"environment":[65],"in":[66],"unsupervised":[68],"manner":[69],"focus":[71],"power":[74],"most":[77],"promising":[78],"spatial":[79],"directions.":[80],"is":[82],"enabled":[83],"by":[84],"architecture":[89],"accounts":[91],"for":[92,156],"constraints":[94],"RF":[97,119,168,188],"chains":[98,169],"models":[100],"transmitter/receiver":[102],"measurement":[103],"as":[105],"complex-valued":[107],"convolutional":[108],"layers.":[109],"Second,":[110],"learns":[114],"how":[115],"construct":[117],"beamforming":[120],"architectures":[125,194],"directly":[126,185],"from":[127],"projected":[129],"vector":[131],"(the":[132],"received":[133],"signal).":[134],"Simulation":[135],"results":[136],"show":[137],"approach":[141],"can":[142],"significantly":[143],"reduce":[144],"overhead":[147],"compared":[148],"classical":[150],"(non-machine":[151],"learning)":[152],"solutions.":[153],"For":[154],"example,":[155],"system":[158],"64":[160,163],"transmit":[161],"receive":[164],"antennas,":[165],"3":[167],"at":[170],"both":[171],"sides,":[172],"solution":[175],"needs":[176],"8":[178],"or":[179],"16":[180],"pilots":[183],"predict":[186],"beamforming/combining":[189],"near-optimal":[197],"achievable":[198],"rates.":[199]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":11}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}