{"id":"https://openalex.org/W4280592889","doi":"https://doi.org/10.1109/wcnc51071.2022.9771840","title":"Evaluation of Adaptation Methods for Deep Learning-based Wi-Fi Receivers","display_name":"Evaluation of Adaptation Methods for Deep Learning-based Wi-Fi Receivers","publication_year":2022,"publication_date":"2022-04-10","ids":{"openalex":"https://openalex.org/W4280592889","doi":"https://doi.org/10.1109/wcnc51071.2022.9771840"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc51071.2022.9771840","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc51071.2022.9771840","pdf_url":null,"source":{"id":"https://openalex.org/S4363607776","display_name":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5076118738","display_name":"William R. Blount","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"William Blount","raw_affiliation_strings":["University of Texas at Austin,Austin,TX,USA,78712"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin,Austin,TX,USA,78712","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111158116","display_name":"Kris Li","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kris Li","raw_affiliation_strings":["Rice University,Houston,TX,USA,77005"],"affiliations":[{"raw_affiliation_string":"Rice University,Houston,TX,USA,77005","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040274740","display_name":"Amrith Lotlikar","orcid":"https://orcid.org/0009-0008-7448-9431"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amrith Lotlikar","raw_affiliation_strings":["Stanford University,Stanford,CA,USA,94305"],"affiliations":[{"raw_affiliation_string":"Stanford University,Stanford,CA,USA,94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061685202","display_name":"Akash Doshi","orcid":"https://orcid.org/0000-0003-0610-2836"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akash Doshi","raw_affiliation_strings":["University of Texas at Austin,Austin,TX,USA,78712"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin,Austin,TX,USA,78712","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042833857","display_name":"Jeffrey G. Andrews","orcid":"https://orcid.org/0000-0002-9115-5088"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeffrey G. Andrews","raw_affiliation_strings":["University of Texas at Austin,Austin,TX,USA,78712"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin,Austin,TX,USA,78712","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076118738"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":0.3222,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.34298346,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":null,"first_page":"656","last_page":"661"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9944000244140625,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9944000244140625,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7143899202346802},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6963452100753784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4303149878978729},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08229625225067139},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.07167932391166687}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7143899202346802},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6963452100753784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4303149878978729},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08229625225067139},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.07167932391166687}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc51071.2022.9771840","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc51071.2022.9771840","pdf_url":null,"source":{"id":"https://openalex.org/S4363607776","display_name":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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":10,"referenced_works":["https://openalex.org/W2604763608","https://openalex.org/W2734408173","https://openalex.org/W2963852693","https://openalex.org/W3084602832","https://openalex.org/W3097372264","https://openalex.org/W3127786530","https://openalex.org/W4294646197","https://openalex.org/W6736057607","https://openalex.org/W6750254146","https://openalex.org/W6782750392"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Machine-learning":[0],"based":[1],"transceivers":[2],"have":[3],"received":[4],"increasing":[5],"attention":[6],"for":[7,81],"next-generation":[8],"wireless":[9],"systems.":[10],"We":[11],"investigate":[12],"the":[13,72,75,78,114,117,137],"application":[14],"of":[15,62,74,77,107,133,140],"two":[16],"meta-learning":[17,41,101],"algorithms":[18,42],"–":[19,27],"Model":[20],"Agnostic":[21],"Meta":[22],"Learning":[23],"(MAML)":[24],"and":[25,35,53,96],"Reptile":[26],"to":[28,71,112],"a":[29,82,104,130],"deep":[30],"learning-based":[31],"Wi-Fi":[32],"channel":[33,57,122],"estimation":[34],"tracking":[36],"system,":[37],"called":[38],"DeepWiPHY.":[39],"The":[40,85,109],"were":[43,66],"compared":[44],"against":[45],"conventional":[46,91],"methods":[47,93,102],"such":[48],"as":[49],"random":[50],"initialization,":[51],"cross-evaluation,":[52],"retraining":[54],"on":[55],"multiple":[56],"models":[58],"with":[59,69],"varying":[60],"severity":[61],"multipath":[63,121],"fading.":[64],"Comparisons":[65],"made":[67],"fairly":[68],"respect":[70],"complexity":[73],"adaptation":[76],"model":[79],"necessary":[80],"new":[83],"environment.":[84],"results":[86],"indicate":[87],"that":[88],"perhaps":[89],"surprisingly,":[90],"training":[92],"are":[94],"adequate":[95],"in":[97],"fact":[98],"can":[99],"outperform":[100],"over":[103],"wide":[105,131],"variety":[106],"channels.":[108],"key":[110],"is":[111],"train":[113],"receiver":[115],"using":[116],"worst-case":[118],"(most":[119],"severe)":[120],"model,":[123],"which":[124],"then":[125],"allows":[126],"strong":[127],"performance":[128],"across":[129],"class":[132],"channels":[134],"without":[135],"requiring":[136],"additional":[138],"burden":[139],"meta-learning.":[141]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}