{"id":"https://openalex.org/W3190898929","doi":null,"title":"Domain Adaptation for Autoencoder-Based End-to-End Communication Over Wireless Channels.","display_name":"Domain Adaptation for Autoencoder-Based End-to-End Communication Over Wireless Channels.","publication_year":2021,"publication_date":"2021-08-02","ids":{"openalex":"https://openalex.org/W3190898929","mag":"3190898929"},"language":"en","primary_location":{"id":"mag:3190898929","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2108.00874","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://export.arxiv.org/pdf/2108.00874","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005867534","display_name":"Jayaram Raghuram","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jayaram Raghuram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048334940","display_name":"Yijing Zeng","orcid":"https://orcid.org/0000-0001-5244-5034","observed_orcids":["https://orcid.org/0000-0001-5244-5034"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yijing Zeng","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5244-5034","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037466874","display_name":"Dolores Garc\u00eda Mart\u00ed","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dolores Garc\u00eda Mart\u00ed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088826068","display_name":"Somesh Jha","orcid":"https://orcid.org/0000-0001-5877-0436","observed_orcids":["https://orcid.org/0000-0001-5877-0436"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Somesh Jha","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5877-0436","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112386897","display_name":"Suman Banerjee","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suman Banerjee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064963698","display_name":"Joerg Widmer","orcid":"https://orcid.org/0000-0001-6667-8779","observed_orcids":["https://orcid.org/0000-0001-6667-8779"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joerg Widmer","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-6667-8779","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5023337421","display_name":"Rafael Ruiz Ortiz","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rafael Ruiz Ortiz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8485999703407288,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8485999703407288,"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"}},{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.08630000054836273,"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"}},{"id":"https://openalex.org/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.00989999994635582,"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/domain-adaptation","display_name":"domain adaptation","score":0.9990000128746033},{"id":"https://openalex.org/keywords/wireless-channel-modeling","display_name":"wireless channel modeling","score":0.6620000004768372},{"id":"https://openalex.org/keywords/end-to-end-learning","display_name":"end-to-end learning","score":0.753000020980835},{"id":"https://openalex.org/keywords/feature-transformation","display_name":"feature transformation","score":0.5699999928474426},{"id":"https://openalex.org/keywords/decoding-error-rate","display_name":"decoding error rate","score":0.5220000147819519}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.928959846496582},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.747246503829956},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6417683959007263},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6173688769340515},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5188556909561157},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5009596347808838},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4193207323551178},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38276493549346924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36088621616363525},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.34289810061454773},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1789911985397339},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3190898929","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2108.00874","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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}],"best_oa_location":{"id":"mag:3190898929","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2108.00874","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W189742998","https://openalex.org/W1493730910","https://openalex.org/W1579853615","https://openalex.org/W1731081199","https://openalex.org/W1882958252","https://openalex.org/W1990505856","https://openalex.org/W2033178790","https://openalex.org/W2104094955","https://openalex.org/W2110091014","https://openalex.org/W2112289862","https://openalex.org/W2131953535","https://openalex.org/W2141253686","https://openalex.org/W2146950091","https://openalex.org/W2547875792","https://openalex.org/W2586793347","https://openalex.org/W2610335499","https://openalex.org/W2616198738","https://openalex.org/W2734408173","https://openalex.org/W2736068844","https://openalex.org/W2795155917","https://openalex.org/W2796257214","https://openalex.org/W2804208583","https://openalex.org/W2904733775","https://openalex.org/W2912280829","https://openalex.org/W2951357534","https://openalex.org/W2962687275","https://openalex.org/W2962695743","https://openalex.org/W2962964572","https://openalex.org/W2963124545","https://openalex.org/W2963139417","https://openalex.org/W2963190722","https://openalex.org/W2963889719","https://openalex.org/W2964121744","https://openalex.org/W3015299720","https://openalex.org/W3106928276","https://openalex.org/W3135006098","https://openalex.org/W3165697121"],"related_works":["https://openalex.org/W4288099791","https://openalex.org/W2900881947","https://openalex.org/W4410228717","https://openalex.org/W4416177236","https://openalex.org/W4401609188","https://openalex.org/W2515594527","https://openalex.org/W3217542280","https://openalex.org/W7020293600","https://openalex.org/W3146885639","https://openalex.org/W7131851855"],"abstract_inverted_index":{"The":[0,227],"problem":[1,130],"of":[2,15,29,85,99,116,131,134,167,194,224,283],"domain":[3,12,21,49,87],"adaptation":[4,126],"conventionally":[5],"considers":[6],"the":[7,44,47,94,97,123,129,143,147,158,164,180,195,198,208,212,221,225,233,240,247,252,267,273,280,284],"setting":[8,45,95],"where":[9,46,96],"a":[10,19,23,56,67,74,82,105,113,135,172,204],"source":[11,253],"has":[13,27,50],"plenty":[14,28],"labeled":[16,37,53],"data,":[17],"and":[18,69,118,146,152,192,214,218,243,260,276],"target":[20,48,86,100,199],"(with":[22],"different":[24],"data":[25,31,54,101,190],"distribution)":[26],"unlabeled":[30],"but":[32],"none":[33],"or":[34,278],"very":[35],"limited":[36,52],"data.":[38],"In":[39],"this":[40],"paper,":[41],"we":[42],"address":[43],"only":[51,81,220],"from":[55],"distribution":[57,98,176,182],"that":[58,266],"is":[59,91],"expected":[60],"to":[61,111,128,156,197,235,246,251],"change":[62],"frequently.":[63],"We":[64,120,202],"first":[65],"propose":[66,203],"fast":[68],"light-weight":[70],"method":[71,90,127,205,228],"for":[72,93,189,206,237],"adapting":[73,207,219],"Gaussian":[75],"mixture":[76],"density":[77],"network":[78],"(MDN)":[79],"using":[80,149],"small":[83],"set":[84],"samples.":[88],"This":[89],"well-suited":[92],"changes":[102,183,238],"rapidly":[103],"(e.g.,":[104],"wireless":[106,136,263],"channel),":[107],"making":[108],"it":[109],"challenging":[110],"collect":[112],"large":[114],"number":[115],"samples":[117,249],"retrain.":[119],"then":[121],"apply":[122],"proposed":[124,268],"MDN":[125,222,274],"end-of-end":[132],"learning":[133],"communication":[137,140],"autoencoder.":[138],"A":[139],"autoencoder":[141,169,196,209,285],"models":[142],"encoder,":[144],"decoder,":[145],"channel":[148,175,181,200,241,288],"neural":[150,216],"networks,":[151,217],"learns":[153],"them":[154],"jointly":[155],"minimize":[157],"overall":[159],"decoding":[160],"error":[161,165,281],"rate.":[162],"However,":[163],"rate":[166,282],"an":[168],"trained":[170],"on":[171,257],"particular":[173],"(source)":[174],"can":[177,270],"degrade":[178],"as":[179],"frequently,":[184],"not":[185],"allowing":[186],"enough":[187],"time":[188],"collection":[191],"retraining":[193],"distribution.":[201,254],"without":[210],"modifying":[211],"encoder":[213],"decoder":[215,234,248],"model":[223],"channel.":[226],"utilizes":[229],"feature":[230],"transformations":[231],"at":[232],"compensate":[236],"in":[239],"distribution,":[242],"effectively":[244],"present":[245],"close":[250],"Experimental":[255],"evaluation":[256],"simulated":[258],"datasets":[259],"real":[261],"mmWave":[262],"channels":[264],"demonstrate":[265],"methods":[269],"quickly":[271],"adapt":[272],"model,":[275],"improve":[277],"maintain":[279],"under":[286],"changing":[287],"conditions.":[289]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-08-16T00:00:00"}
