{"id":"https://openalex.org/W3081039003","doi":"https://doi.org/10.1109/tccn.2020.3019604","title":"Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems","display_name":"Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems","publication_year":2020,"publication_date":"2020-08-26","ids":{"openalex":"https://openalex.org/W3081039003","doi":"https://doi.org/10.1109/tccn.2020.3019604","mag":"3081039003"},"language":"en","primary_location":{"id":"doi:10.1109/tccn.2020.3019604","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2020.3019604","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"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 Cognitive Communications and Networking","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/A5010494524","display_name":"Shaocheng Huang","orcid":"https://orcid.org/0000-0001-8375-2897"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Shaocheng Huang","raw_affiliation_strings":["Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100421130","display_name":"Yu Ye","orcid":"https://orcid.org/0000-0003-4686-0973"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Yu Ye","raw_affiliation_strings":["Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037292846","display_name":"Ming Xiao","orcid":"https://orcid.org/0000-0002-5407-0835"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ming Xiao","raw_affiliation_strings":["Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010494524"],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":2.5686,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.90162005,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"7","issue":"1","first_page":"120","last_page":"132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12791","display_name":"Full-Duplex Wireless Communications","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/T12791","display_name":"Full-Duplex Wireless Communications","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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9990000128746033,"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/T11392","display_name":"Energy Harvesting in Wireless Networks","score":0.9976000189781189,"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":0.7584347724914551},{"id":"https://openalex.org/keywords/spectral-efficiency","display_name":"Spectral efficiency","score":0.7278458476066589},{"id":"https://openalex.org/keywords/beamforming","display_name":"Beamforming","score":0.7142699956893921},{"id":"https://openalex.org/keywords/matching-pursuit","display_name":"Matching pursuit","score":0.5738356113433838},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.5082223415374756},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.49505558609962463},{"id":"https://openalex.org/keywords/channel-state-information","display_name":"Channel state information","score":0.4874579906463623},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.4444088935852051},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4364224076271057},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4168601632118225},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.4160955846309662},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3933083415031433},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.39092761278152466},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.33028870820999146},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.286601722240448},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26080602407455444},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1846732795238495},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1809464991092682},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.13376468420028687},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10403174161911011}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7584347724914551},{"id":"https://openalex.org/C137246740","wikidata":"https://www.wikidata.org/wiki/Q583970","display_name":"Spectral efficiency","level":3,"score":0.7278458476066589},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.7142699956893921},{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.5738356113433838},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.5082223415374756},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.49505558609962463},{"id":"https://openalex.org/C148063708","wikidata":"https://www.wikidata.org/wiki/Q5072511","display_name":"Channel state information","level":3,"score":0.4874579906463623},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.4444088935852051},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4364224076271057},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4168601632118225},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.4160955846309662},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3933083415031433},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.39092761278152466},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.33028870820999146},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.286601722240448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26080602407455444},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1846732795238495},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1809464991092682},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.13376468420028687},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10403174161911011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tccn.2020.3019604","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2020.3019604","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"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 Cognitive Communications and Networking","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1992463666","https://openalex.org/W1993717606","https://openalex.org/W2026131661","https://openalex.org/W2053521124","https://openalex.org/W2099579348","https://openalex.org/W2111072639","https://openalex.org/W2131329059","https://openalex.org/W2133884101","https://openalex.org/W2137983211","https://openalex.org/W2164278908","https://openalex.org/W2259391824","https://openalex.org/W2272804037","https://openalex.org/W2508393166","https://openalex.org/W2520728844","https://openalex.org/W2561775801","https://openalex.org/W2615358882","https://openalex.org/W2620529177","https://openalex.org/W2739592646","https://openalex.org/W2739778833","https://openalex.org/W2770769351","https://openalex.org/W2780231131","https://openalex.org/W2790223875","https://openalex.org/W2807133510","https://openalex.org/W2810871807","https://openalex.org/W2810971439","https://openalex.org/W2888031968","https://openalex.org/W2904019619","https://openalex.org/W2909191390","https://openalex.org/W2909794597","https://openalex.org/W2920611773","https://openalex.org/W2941042367","https://openalex.org/W2943928690","https://openalex.org/W2962956060","https://openalex.org/W2963903846","https://openalex.org/W2975471211","https://openalex.org/W2991873439","https://openalex.org/W2992096220","https://openalex.org/W2999712817","https://openalex.org/W3010410501","https://openalex.org/W3018472673","https://openalex.org/W3103926693","https://openalex.org/W3104804153","https://openalex.org/W3146803896","https://openalex.org/W4288364567","https://openalex.org/W4292363360"],"related_works":["https://openalex.org/W2042644197","https://openalex.org/W2073241848","https://openalex.org/W2067443264","https://openalex.org/W2785927776","https://openalex.org/W2020549994","https://openalex.org/W4220674950","https://openalex.org/W2381915087","https://openalex.org/W2034362047","https://openalex.org/W2101989320","https://openalex.org/W2137446833"],"abstract_inverted_index":{"Millimeter":[0],"Wave":[1],"(mmWave)":[2],"communications":[3],"with":[4,18,55,230],"full-duplex":[5],"(FD)":[6],"have":[7],"the":[8,12,21,36,49,80,148,159,197,212,216],"potential":[9],"of":[10,82,121,201,219,228],"increasing":[11],"spectral":[13,187],"efficiency,":[14],"relative":[15],"to":[16,31,47,92,126,190],"those":[17],"half-duplex.":[19],"However,":[20,58],"residual":[22],"self-interference":[23],"(SI)":[24],"from":[25],"FD":[26,63,96],"and":[27,52,106,131,142,157,181,233],"high":[28],"pathloss":[29],"inherent":[30],"mmWave":[32,64,97],"signals":[33],"may":[34],"degrade":[35],"system":[37],"performance.":[38],"Meanwhile,":[39],"hybrid":[40,160],"beamforming":[41],"(HBF)":[42],"is":[43,206,221],"an":[44,117],"efficient":[45],"technology":[46],"enhance":[48],"channel":[50,83],"gain":[51],"mitigate":[53],"interference":[54],"reasonable":[56],"complexity.":[57],"conventional":[59],"HBF":[60,94,104,111,144,179],"approaches":[61],"for":[62,95,139],"systems":[65],"are":[66,72],"based":[67,103,110,124,137,173,204],"on":[68,79],"optimization":[69],"processes,":[70],"which":[71],"either":[73],"too":[74],"complex":[75],"or":[76],"strongly":[77],"rely":[78],"quality":[81],"state":[84],"information":[85],"(CSI).":[86],"We":[87],"propose":[88,116],"two":[89],"learning":[90,101,149,172,203],"schemes":[91,174,205],"design":[93],"systems,":[98],"i.e.,":[99],"extreme":[100],"machine":[102],"(ELM-HBF)":[105],"convolutional":[107],"neural":[108],"networks":[109],"(CNN-HBF).":[112],"Specifically,":[113],"we":[114,151],"first":[115],"alternating":[118],"direction":[119],"method":[120],"multipliers":[122],"(ADMM)":[123],"algorithm":[125,138],"achieve":[127,182],"SI":[128],"cancellation":[129],"beamforming,":[130],"then":[132],"use":[133],"a":[134],"majorization-minimization":[135],"(MM)":[136],"joint":[140],"transmitting":[141,232],"receiving":[143,234],"optimization.":[145],"To":[146],"train":[147],"networks,":[150],"simulate":[152],"noisy":[153],"channels":[154],"as":[155,166],"input,":[156],"select":[158],"beamformers":[161],"calculated":[162],"by":[163],"proposed":[164,202],"algorithms":[165],"targets.":[167],"Results":[168],"show":[169],"that":[170,227],"both":[171],"can":[175],"provide":[176],"more":[177],"robust":[178],"performance":[180],"at":[183],"least":[184],"22.1%":[185],"higher":[186],"efficiency":[188],"compared":[189],"orthogonal":[191],"matching":[192],"pursuit":[193],"(OMP)":[194],"algorithms.":[195],"Besides,":[196],"online":[198],"prediction":[199],"time":[200,218],"almost":[207],"20":[208],"times":[209,224],"faster":[210,225],"than":[211,226],"OMP":[213],"scheme.":[214],"Furthermore,":[215],"training":[217],"ELM-HBF":[220],"about":[222],"600":[223],"CNN-HBF":[229],"64":[231],"antennas.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}