{"id":"https://openalex.org/W7155090083","doi":"https://doi.org/10.1109/access.2026.3685689","title":"ResNet-Based Deep Learning for OTFS Channel Estimation With Pilot Superimposition","display_name":"ResNet-Based Deep Learning for OTFS Channel Estimation With Pilot Superimposition","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7155090083","doi":"https://doi.org/10.1109/access.2026.3685689"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3685689","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3685689","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3685689","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Junlong Wang","orcid":"https://orcid.org/0009-0001-7265-6442"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Junlong Wang","raw_affiliation_strings":["Department of Computer and Communication Engineering, Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0001-7265-6442","affiliations":[{"raw_affiliation_string":"Department of Computer and Communication Engineering, Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091321696","display_name":"Zhenni Pan","orcid":"https://orcid.org/0000-0002-5332-6923"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhenni Pan","raw_affiliation_strings":["Department of Computer and Communication Engineering, Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Communication Engineering, Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025087534","display_name":"Shigeru Shimamoto","orcid":"https://orcid.org/0000-0002-2266-7362"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeru Shimamoto","raw_affiliation_strings":["Department of Computer and Communication Engineering, Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-2266-7362","affiliations":[{"raw_affiliation_string":"Department of Computer and Communication Engineering, Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006124696","display_name":"Tu Dac Ho","orcid":"https://orcid.org/0000-0001-7215-0479"},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Tu Dac Ho","raw_affiliation_strings":["Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Trondheim, Norway"],"raw_orcid":"https://orcid.org/0000-0001-7215-0479","affiliations":[{"raw_affiliation_string":"Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Trondheim, Norway","institution_ids":["https://openalex.org/I204778367"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46383436,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"66054","last_page":"66068"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.8341000080108643,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.8341000080108643,"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/T11873","display_name":"PAPR reduction in OFDM","score":0.04340000078082085,"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/T10232","display_name":"Optical Network Technologies","score":0.020600000396370888,"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/superimposition","display_name":"Superimposition","score":0.8335000276565552},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6466000080108643},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.635699987411499},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5295000076293945},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.31310001015663147},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3073999881744385}],"concepts":[{"id":"https://openalex.org/C143874112","wikidata":"https://www.wikidata.org/wiki/Q7643452","display_name":"Superimposition","level":2,"score":0.8335000276565552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.741100013256073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6837000250816345},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6466000080108643},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.635699987411499},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5295000076293945},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45840001106262207},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2897999882698059},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3685689","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3685689","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:eee66e3033ef40e08ffc916238a10245","is_oa":true,"landing_page_url":"https://doaj.org/article/eee66e3033ef40e08ffc916238a10245","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 66054-66068 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3685689","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3685689","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5188290483","display_name":"\u77e5\u7684\u74b0\u5883\u306e\u305f\u3081\u306e\u30bb\u30f3\u30b7\u30f3\u30b0\u30fb\u30a2\u30af\u30c1\u30e5\u30a8\u30fc\u30b7\u30e7\u30f3\u30fb\u901a\u4fe1\u53ca\u3073\u77e5\u7684\u4fe1\u53f7\u51e6\u7406\u57fa\u76e4\u306b\u95a2\u3059\u308b\u7814\u7a76","funder_award_id":"JPMJAP2326","funder_id":"https://openalex.org/F4320334789","funder_display_name":"Japan Science and Technology Agency"}],"funders":[{"id":"https://openalex.org/F4320326569","display_name":"Research Institute of Food Science and Technology","ror":null},{"id":"https://openalex.org/F4320334789","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2024591385","https://openalex.org/W2194775991","https://openalex.org/W2389426138","https://openalex.org/W2412917545","https://openalex.org/W2469797310","https://openalex.org/W2612254130","https://openalex.org/W2888002205","https://openalex.org/W2908111164","https://openalex.org/W2951947870","https://openalex.org/W2959562235","https://openalex.org/W2962951066","https://openalex.org/W2963986627","https://openalex.org/W2964005248","https://openalex.org/W2981096252","https://openalex.org/W3011830499","https://openalex.org/W3172410657","https://openalex.org/W3186159221","https://openalex.org/W3189209457","https://openalex.org/W3210414465","https://openalex.org/W4200398952","https://openalex.org/W4205257518","https://openalex.org/W4233476362","https://openalex.org/W4285039009","https://openalex.org/W4297310172","https://openalex.org/W4297310181","https://openalex.org/W4297310221","https://openalex.org/W4317418944","https://openalex.org/W4380607233","https://openalex.org/W4384518558","https://openalex.org/W4391021672","https://openalex.org/W4401878956","https://openalex.org/W4401943628","https://openalex.org/W4404132801","https://openalex.org/W4405812479","https://openalex.org/W4416649684","https://openalex.org/W7125833804"],"related_works":[],"abstract_inverted_index":{"The":[0,116],"rapid":[1],"growth":[2],"of":[3,79],"the":[4,63,66,74,80,85,143],"information":[5],"industry":[6],"imposes":[7],"strict":[8],"demands":[9],"on":[10],"future":[11],"wireless":[12],"communication":[13],"systems,":[14],"especially":[15],"in":[16,39,90,112],"high-mobility":[17,157],"vehicular":[18],"scenarios.":[19],"To":[20],"address":[21],"these":[22],"challenges,":[23],"we":[24,45],"employ":[25],"orthogonal":[26],"time":[27],"frequency":[28],"space":[29],"(OTFS)":[30],"modulation":[31],"to":[32,72,105],"mitigate":[33],"Doppler":[34],"effects":[35],"and":[36,55,109,128,147,151],"enhance":[37],"robustness":[38],"high-speed":[40],"environments.":[41],"In":[42],"this":[43],"paper,":[44],"examine":[46],"a":[47,101],"superimposed":[48,91],"pilot":[49],"technique":[50],"for":[51],"OTFS":[52],"channel":[53,95],"estimation":[54,96,144],"introduce":[56],"an":[57],"optimized":[58],"two-stage":[59],"ResNet-based":[60],"architecture":[61],"at":[62],"receiver.":[64],"Specifically,":[65],"ResNet":[67],"core":[68],"leverages":[69],"residual":[70],"connections":[71],"extract":[73],"2D":[75],"macroscopic":[76],"structural":[77],"envelope":[78],"delay-Doppler":[81],"channel,":[82],"effectively":[83],"decoupling":[84],"severe":[86],"pilot-data":[87],"interference":[88],"inherent":[89],"schemes.":[92],"Furthermore,":[93],"historical":[94],"data":[97],"are":[98],"integrated":[99],"as":[100],"locally":[102],"quasi-stationary":[103],"reference":[104],"prevent":[106],"noise":[107],"overfitting":[108],"increase":[110],"accuracy":[111],"subsequent":[113],"tracking":[114],"tasks.":[115],"system":[117],"performance":[118],"is":[119],"comprehensively":[120],"evaluated":[121],"using":[122],"Normalized":[123],"Mean":[124],"Square":[125],"Error":[126,130],"(NMSE)":[127],"Bit":[129],"Rate":[131],"(BER)":[132],"metrics.":[133],"Simulation":[134],"results":[135],"demonstrate":[136],"that":[137],"our":[138],"proposed":[139],"method":[140],"significantly":[141],"lowers":[142],"error":[145],"floor":[146],"outperforms":[148],"existing":[149],"traditional":[150],"deep":[152],"learning-based":[153],"approaches":[154],"under":[155],"various":[156],"conditions.":[158]},"counts_by_year":[],"updated_date":"2026-05-07T06:04:25.777469","created_date":"2026-04-22T00:00:00"}