{"id":"https://openalex.org/W4414405983","doi":"https://doi.org/10.1109/iccworkshops67674.2025.11162298","title":"Domain Adaptation in Beam Management using Asymmetric Autoencoder and Transfer Learning","display_name":"Domain Adaptation in Beam Management using Asymmetric Autoencoder and Transfer Learning","publication_year":2025,"publication_date":"2025-06-08","ids":{"openalex":"https://openalex.org/W4414405983","doi":"https://doi.org/10.1109/iccworkshops67674.2025.11162298"},"language":"en","primary_location":{"id":"doi:10.1109/iccworkshops67674.2025.11162298","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccworkshops67674.2025.11162298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Communications Workshops (ICC Workshops)","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/A5030447120","display_name":"Md. Rakibul Hoque","orcid":"https://orcid.org/0000-0002-4516-1081"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Mafuzal Hoque","raw_affiliation_strings":["University of Ottawa,School of Electrical Engineering and Computer Science,Ottawa,Canada"],"affiliations":[{"raw_affiliation_string":"University of Ottawa,School of Electrical Engineering and Computer Science,Ottawa,Canada","institution_ids":["https://openalex.org/I153718931"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049344765","display_name":"Medhat Elsayed","orcid":"https://orcid.org/0000-0002-1106-6078"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Medhat Elsayed","raw_affiliation_strings":["University of Ottawa,School of Electrical Engineering and Computer Science,Ottawa,Canada"],"affiliations":[{"raw_affiliation_string":"University of Ottawa,School of Electrical Engineering and Computer Science,Ottawa,Canada","institution_ids":["https://openalex.org/I153718931"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089891162","display_name":"Melike Erol\u2010Kantarci","orcid":"https://orcid.org/0000-0001-6787-8457"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Melike Erol-Kantarci","raw_affiliation_strings":["University of Ottawa,School of Electrical Engineering and Computer Science,Ottawa,Canada"],"affiliations":[{"raw_affiliation_string":"University of Ottawa,School of Electrical Engineering and Computer Science,Ottawa,Canada","institution_ids":["https://openalex.org/I153718931"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030447120"],"corresponding_institution_ids":["https://openalex.org/I153718931"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2710478,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1233","last_page":"1238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14158","display_name":"Optical Systems and Laser Technology","score":0.6220999956130981,"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/T14158","display_name":"Optical Systems and Laser Technology","score":0.6220999956130981,"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/autoencoder","display_name":"Autoencoder","score":0.7480999827384949},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6638000011444092},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.46219998598098755},{"id":"https://openalex.org/keywords/beam-search","display_name":"Beam search","score":0.4586000144481659},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.42559999227523804},{"id":"https://openalex.org/keywords/beamforming","display_name":"Beamforming","score":0.41839998960494995},{"id":"https://openalex.org/keywords/beam","display_name":"Beam (structure)","score":0.40220001339912415},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.3481999933719635},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.328900009393692},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.32829999923706055}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7480999827384949},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6638000011444092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.628600001335144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49729999899864197},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.46219998598098755},{"id":"https://openalex.org/C19889080","wikidata":"https://www.wikidata.org/wiki/Q2835852","display_name":"Beam search","level":3,"score":0.4586000144481659},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4302000105381012},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C168834538","wikidata":"https://www.wikidata.org/wiki/Q3705329","display_name":"Beam (structure)","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34209999442100525},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.32829999923706055},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.27410000562667847},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C148063708","wikidata":"https://www.wikidata.org/wiki/Q5072511","display_name":"Channel state information","level":3,"score":0.271699994802475},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.2513999938964844},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccworkshops67674.2025.11162298","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccworkshops67674.2025.11162298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Communications Workshops (ICC Workshops)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2766328202","https://openalex.org/W3113734015","https://openalex.org/W3121955719","https://openalex.org/W4309354181","https://openalex.org/W4364857877","https://openalex.org/W4367315342","https://openalex.org/W4383112908","https://openalex.org/W4387883605","https://openalex.org/W4388206720","https://openalex.org/W4391234088","https://openalex.org/W4399169404","https://openalex.org/W4400276678","https://openalex.org/W4401358750"],"related_works":[],"abstract_inverted_index":{"In":[0],"5G":[1],"millimeter":[2],"wave":[3],"(mmWave)":[4],"networks,":[5],"beam":[6,14,45,69,167,225,239],"management":[7,226],"is":[8],"crucial":[9],"for":[10,166],"establishing":[11],"optimal":[12,238],"transmitter-receiver":[13],"pairs":[15],"between":[16,96],"the":[17,72,178,190,243],"base":[18],"station":[19],"(gNB)":[20],"and":[21,30,33,98,135,147,181,212,233,241,251],"user":[22],"equipment":[23],"(UE)":[24],"through":[25],"sequential":[26],"adjustment":[27],"of":[28,133],"azimuth":[29],"elevation":[31],"angles":[32],"transmit":[34],"power.":[35],"Conventional":[36],"approaches":[37],"like":[38],"exhaustive":[39],"search":[40,74],"increase":[41],"exponentially":[42],"with":[43,123,151,173,192,202,255],"growing":[44],"configurations,":[46],"while":[47,236],"predefined":[48],"codebook-based":[49],"beamforming":[50],"vectors":[51],"yield":[52],"suboptimal":[53],"solutions":[54,65],"in":[55,215,231,248,253],"dynamic":[56],"networks.":[57],"Although":[58],"machine":[59],"learning":[60,67,79,83,164,172],"(ML)":[61],"techniques":[62],"offer":[63],"potential":[64],"by":[66,246],"efficient":[68],"selection":[70],"policies,":[71],"high-dimensional":[73],"space":[75,142,149],"makes":[76],"direct":[77],"policy":[78],"computationally":[80],"intensive.":[81],"Transfer":[82],"(TL)":[84],"can":[85],"leverage":[86],"knowledge":[87],"from":[88],"simpler":[89],"network":[90],"optimization":[91],"tasks;":[92],"however,":[93],"dimensional":[94,107],"mismatches":[95],"source":[97,134,184,194],"target":[99,136],"domains":[100],"present":[101,110],"significant":[102],"challenges.":[103],"To":[104],"address":[105],"these":[106],"mismatches,":[108],"we":[109,160],"a":[111,169],"novel":[112],"domain":[113,195],"adaptation":[114],"(DA)":[115],"framework":[116],"that":[117],"uses":[118],"an":[119,182],"asymmetric":[120],"autoencoder":[121],"combined":[122],"Maximum":[124],"Mean":[125],"Discrepancy":[126],"(MMD)":[127],"loss":[128,153],"to":[129,199,228],"align":[130],"latent":[131,148],"representations":[132],"domains.":[137],"This":[138],"architecture":[139],"achieves":[140],"state":[141],"reconstruction":[143],"accuracy":[144],"exceeding":[145],"93%":[146],"alignment":[150],"total":[152],"below":[154],"0.01.":[155],"Building":[156],"on":[157],"this":[158],"framework,":[159],"introduce":[161],"two":[162],"transfer":[163,171,186],"methods":[165,206,222],"management:":[168],"representation":[170],"fine-tuning":[174],"(RTLF)":[175],"approach":[176],"leveraging":[177],"pretrained":[179],"encoder,":[180],"adaptive":[183],"weight":[185,196],"(ASWTR)":[187],"mechanism":[188],"combining":[189],"encoder":[191],"selective":[193],"transfer.":[197],"Compared":[198],"baseline":[200,229],"training":[201,216],"DRL,":[203],"both":[204],"TL":[205,221],"demonstrate":[207,223],"minimum":[208],"16%":[209],"faster":[210],"convergence":[211],"81%":[213],"reduction":[214],"episode":[217],"steps.":[218],"The":[219],"proposed":[220],"comparable":[224],"performance":[227],"DRL":[230],"throughput":[232,254],"energy":[234,249],"efficiency,":[235],"maintaining":[237],"configurations":[240],"outperforming":[242],"non-DRL":[244],"method":[245],"50%":[247],"efficiency":[250],"45%":[252],"20":[256],"UEs.":[257]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}