{"id":"https://openalex.org/W2913261975","doi":"https://doi.org/10.1109/apcc.2018.8633453","title":"Pilot Assignment and Channel Estimation via Deep Neural Network","display_name":"Pilot Assignment and Channel Estimation via Deep Neural Network","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2913261975","doi":"https://doi.org/10.1109/apcc.2018.8633453","mag":"2913261975"},"language":"en","primary_location":{"id":"doi:10.1109/apcc.2018.8633453","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apcc.2018.8633453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th Asia-Pacific Conference on Communications (APCC)","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/A5109399467","display_name":"Seunghwan Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seunghwan Lee","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033338397","display_name":"Hyungyu Ju","orcid":"https://orcid.org/0000-0002-2841-3400"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyungyu Ju","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076075267","display_name":"Byonghyo Shim","orcid":"https://orcid.org/0000-0001-5051-1763"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byonghyo Shim","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5109399467"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":0.5233,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.69547689,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"454","last_page":"458"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10125","display_name":"Advanced Wireless Communication Techniques","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/T10125","display_name":"Advanced Wireless Communication Techniques","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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11873","display_name":"PAPR reduction in OFDM","score":0.9987000226974487,"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":0.7394810318946838},{"id":"https://openalex.org/keywords/orthogonal-frequency-division-multiplexing","display_name":"Orthogonal frequency-division multiplexing","score":0.7312597036361694},{"id":"https://openalex.org/keywords/transmitter","display_name":"Transmitter","score":0.7009159922599792},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6808891892433167},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6725686192512512},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5806931853294373},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4995999336242676},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4976229965686798},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4154930114746094},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.28914937376976013},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2779979407787323},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2653445601463318}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7394810318946838},{"id":"https://openalex.org/C40409654","wikidata":"https://www.wikidata.org/wiki/Q375889","display_name":"Orthogonal frequency-division multiplexing","level":3,"score":0.7312597036361694},{"id":"https://openalex.org/C47798520","wikidata":"https://www.wikidata.org/wiki/Q190157","display_name":"Transmitter","level":3,"score":0.7009159922599792},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6808891892433167},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6725686192512512},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5806931853294373},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4995999336242676},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4976229965686798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4154930114746094},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28914937376976013},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2779979407787323},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2653445601463318}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apcc.2018.8633453","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apcc.2018.8633453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th Asia-Pacific Conference on Communications (APCC)","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/W107325040","https://openalex.org/W1968192955","https://openalex.org/W2009171710","https://openalex.org/W2092235212","https://openalex.org/W2114004090","https://openalex.org/W2122072238","https://openalex.org/W2133696480","https://openalex.org/W2149587304","https://openalex.org/W2154395274","https://openalex.org/W2557283755","https://openalex.org/W2602457796","https://openalex.org/W6678188153","https://openalex.org/W6682481464"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W2909431601"],"abstract_inverted_index":{"In":[0,39,65],"orthogonal":[1],"frequency":[2],"division":[3],"multiplexing":[4],"(OFDM)":[5],"systems,":[6,41],"channel":[7,30],"estimation":[8,31],"is":[9,44],"by":[10,99],"far":[11],"the":[12,17,21,27,49,57,84,90,108,112],"most":[13],"important":[14],"operation":[15],"in":[16],"receiver":[18],"to":[19,106],"ensure":[20],"accurate":[22],"detection":[23],"and":[24,51],"decoding.":[25],"Over":[26],"years,":[28],"pilot-aided":[29],"has":[32,61],"been":[33,62],"widely":[34,63],"used":[35],"for":[36],"this":[37,66],"purpose.":[38],"open-loop":[40],"since":[42],"there":[43],"no":[45],"feedback":[46],"link":[47],"between":[48],"transmitter":[50],"receiver,":[52],"an":[53],"approach":[54],"based":[55,76],"on":[56,77],"equi-spaced":[58],"pilot":[59,73,93],"assignment":[60],"employed.":[64],"paper,":[67],"we":[68,87],"propose":[69],"a":[70,100],"closed-loop":[71],"non-uniform":[72],"allocation":[74,94],"strategy":[75],"deep":[78],"neural":[79],"network":[80],"(DNN)":[81],"technique.":[82],"From":[83],"numerical":[85],"evaluations,":[86],"show":[88],"that":[89],"proposed":[91],"autoencoder-based":[92],"technique":[95],"outperforms":[96],"conventional":[97],"approaches":[98],"large":[101],"margin,":[102],"demonstrating":[103],"its":[104],"ability":[105],"learn":[107],"statistical":[109],"characteristics":[110],"of":[111],"wireless":[113],"channel.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
