{"id":"https://openalex.org/W4415482697","doi":"https://doi.org/10.1109/lwc.2025.3624843","title":"CMENet: Laplacian Pyramid Architecture-Based Deep Learning for OFDM Channel Estimation","display_name":"CMENet: Laplacian Pyramid Architecture-Based Deep Learning for OFDM Channel Estimation","publication_year":2025,"publication_date":"2025-10-23","ids":{"openalex":"https://openalex.org/W4415482697","doi":"https://doi.org/10.1109/lwc.2025.3624843"},"language":null,"primary_location":{"id":"doi:10.1109/lwc.2025.3624843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lwc.2025.3624843","pdf_url":null,"source":{"id":"https://openalex.org/S2500830676","display_name":"IEEE Wireless Communications Letters","issn_l":"2162-2337","issn":["2162-2337","2162-2345"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Wireless Communications Letters","raw_type":"journal-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/A5068420920","display_name":"Xuefeng Wang","orcid":"https://orcid.org/0000-0001-5427-7070"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xuefeng Wang","raw_affiliation_strings":["State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5427-7070","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000004323","display_name":"Yang Lu","orcid":"https://orcid.org/0000-0002-3519-4488"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Lu","raw_affiliation_strings":["State Key Laboratory of Advanced Rail Autonomous Operation and the School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3519-4488","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation and the School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100344294","display_name":"Wei Chen","orcid":"https://orcid.org/0000-0001-5090-9915"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Chen","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5090-9915","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061813280","display_name":"Chong\u2010Yung Chi","orcid":"https://orcid.org/0000-0001-5004-7155"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chong-Yung Chi","raw_affiliation_strings":["Department of Electrical Engineering, Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0001-5004-7155","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100620739","display_name":"Bo Ai","orcid":"https://orcid.org/0000-0001-6850-0595"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Ai","raw_affiliation_strings":["State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6850-0595","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091266202","display_name":"Dusit Niyato","orcid":"https://orcid.org/0000-0002-7442-7416"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Dusit Niyato","raw_affiliation_strings":["College of Computing and Data Science, Nanyang Technological University, Nanyang Avenue, Singapore","College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-7442-7416","affiliations":[{"raw_affiliation_string":"College of Computing and Data Science, Nanyang Technological University, Nanyang Avenue, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5068420920"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15403814,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":null,"first_page":"240","last_page":"244"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9805999994277954,"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":0.9805999994277954,"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":0.9104999899864197,"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/T13905","display_name":"Telecommunications and Broadcasting Technologies","score":0.9060999751091003,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/deep-learning","display_name":"Deep learning","score":0.6455000042915344},{"id":"https://openalex.org/keywords/orthogonal-frequency-division-multiplexing","display_name":"Orthogonal frequency-division multiplexing","score":0.6280999779701233},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6089000105857849},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5268999934196472},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5092999935150146},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5077999830245972},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.45980000495910645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45249998569488525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7639999985694885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6455000042915344},{"id":"https://openalex.org/C40409654","wikidata":"https://www.wikidata.org/wiki/Q375889","display_name":"Orthogonal frequency-division multiplexing","level":3,"score":0.6280999779701233},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6089000105857849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5672000050544739},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5268999934196472},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5092999935150146},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5077999830245972},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.45980000495910645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45249998569488525},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4318000078201294},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C19275194","wikidata":"https://www.wikidata.org/wiki/Q222903","display_name":"Multiplexing","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C130946814","wikidata":"https://www.wikidata.org/wiki/Q5988271","display_name":"MIMO-OFDM","level":4,"score":0.3312999904155731},{"id":"https://openalex.org/C207987634","wikidata":"https://www.wikidata.org/wiki/Q176862","display_name":"MIMO","level":3,"score":0.30889999866485596},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2632000148296356},{"id":"https://openalex.org/C165700671","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Laplace operator","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lwc.2025.3624843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lwc.2025.3624843","pdf_url":null,"source":{"id":"https://openalex.org/S2500830676","display_name":"IEEE Wireless Communications Letters","issn_l":"2162-2337","issn":["2162-2337","2162-2345"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Wireless Communications Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5331489779","display_name":null,"funder_award_id":"62221001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8194416217","display_name":null,"funder_award_id":"2025JBXT010","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8765633020","display_name":null,"funder_award_id":"U2468201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G948193724","display_name":null,"funder_award_id":"62571023","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2014002306","https://openalex.org/W2103504761","https://openalex.org/W2607041014","https://openalex.org/W2752782242","https://openalex.org/W2963836746","https://openalex.org/W2982083293","https://openalex.org/W2996956941","https://openalex.org/W3115825813","https://openalex.org/W4225153276","https://openalex.org/W4321195586","https://openalex.org/W4387870336","https://openalex.org/W4388739137","https://openalex.org/W4400276487","https://openalex.org/W4401539884","https://openalex.org/W4404565255","https://openalex.org/W4406754298","https://openalex.org/W4412623287"],"related_works":[],"abstract_inverted_index":{"By":[0],"judiciously":[1],"treating":[2],"the":[3,49,62,65,78,83,92,114,121],"channel":[4,27,55,79,94,110],"estimation":[5,28,127],"problem":[6],"as":[7],"a":[8,14,20,40,97],"super-resolution":[9],"problem,":[10],"this":[11],"paper":[12],"proposes":[13],"cascaded":[15],"multiscale":[16],"efficient":[17],"network":[18],"(CMENet),":[19],"novel":[21],"deep":[22],"learning":[23],"(DL)-based":[24],"approach":[25],"for":[26],"in":[29,96],"orthogonal":[30],"frequency":[31],"division":[32],"multiplexing":[33],"(OFDM)":[34],"systems.":[35],"The":[36],"proposed":[37,115,122],"CMENet":[38,123],"employs":[39],"Laplacian":[41],"pyramid":[42],"architecture":[43],"with":[44,70],"dual":[45],"branches,":[46],"composed":[47],"of":[48],"feature":[50,80],"extraction":[51],"branch":[52,57],"(FEB)":[53],"and":[54,82],"reconstruction":[56],"(CRB)":[58],"operating":[59],"concurrently,":[60],"where":[61],"former":[63],"utilizes":[64,85],"Squeeze-and-Excitation":[66],"Network":[67],"(SENet)":[68],"together":[69],"Mobile":[71],"Inverted":[72],"Bottleneck":[73],"Convolution":[74],"(MBConv)":[75],"to":[76,90,112],"enhance":[77],"extraction,":[81],"latter":[84],"residual":[86],"convolutional":[87],"neural":[88],"networks":[89],"refine":[91],"least-squares":[93],"estimates":[95],"coarse-to-fine":[98],"manner.":[99],"Extensive":[100],"experiments":[101],"are":[102],"conducted":[103],"on":[104],"3rd":[105],"Generation":[106],"Partnership":[107],"Project":[108],"(3GPP)":[109],"models":[111],"evaluate":[113],"CMENet.":[116],"Numerical":[117],"results":[118],"demonstrate":[119],"that":[120],"can":[124],"achieve":[125],"superior":[126],"accuracy":[128],"over":[129],"state-of-the-art":[130],"DL":[131],"models.":[132]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-23T00:00:00"}
