{"id":"https://openalex.org/W2916088741","doi":"https://doi.org/10.1109/access.2019.2900715","title":"WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels","display_name":"WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2916088741","doi":"https://doi.org/10.1109/access.2019.2900715","mag":"2916088741"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2900715","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2900715","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08648366.pdf","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":null,"license_id":null,"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://ieeexplore.ieee.org/ielx7/6287639/8600701/08648366.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056273339","display_name":"Hancheng Lu","orcid":"https://orcid.org/0000-0001-8302-4996"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hancheng Lu","raw_affiliation_strings":["Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0001-8302-4996","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037489231","display_name":"Bo Lei","orcid":"https://orcid.org/0000-0001-6011-5262"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Bo","raw_affiliation_strings":["Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"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":2.1364,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.86270068,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"24440","last_page":"24451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9976999759674072,"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":0.8214306831359863},{"id":"https://openalex.org/keywords/fading","display_name":"Fading","score":0.7981687784194946},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7177159190177917},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7089152336120605},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6933727860450745},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.5563856959342957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5079382061958313},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5024750232696533},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.497130423784256},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.48100098967552185},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4692544639110565},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.337415874004364},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.24547746777534485},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.22078365087509155}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8214306831359863},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.7981687784194946},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7177159190177917},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7089152336120605},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6933727860450745},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.5563856959342957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5079382061958313},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5024750232696533},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.497130423784256},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.48100098967552185},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4692544639110565},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.337415874004364},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.24547746777534485},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.22078365087509155},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2900715","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2900715","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08648366.pdf","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9089dcd3a46540a7b73b5457518f4b8a","is_oa":true,"landing_page_url":"https://doaj.org/article/9089dcd3a46540a7b73b5457518f4b8a","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 7, Pp 24440-24451 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2900715","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2900715","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08648366.pdf","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":null,"license_id":null,"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/G4220777412","display_name":null,"funder_award_id":"61631017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4239754536","display_name":"\u65e0\u7ebf\u5f02\u6784\u73af\u5883\u4e0b\u6df7\u5408\u6570\u6a21\u89c6\u9891\u4f20\u8f93\u673a\u5236\u7814\u7a76","funder_award_id":"61771445","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7079021369","display_name":null,"funder_award_id":"91538203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"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":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2916088741.pdf","grobid_xml":"https://content.openalex.org/works/W2916088741.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W1665214252","https://openalex.org/W1951319388","https://openalex.org/W1976709621","https://openalex.org/W1978338723","https://openalex.org/W1980038761","https://openalex.org/W1992405901","https://openalex.org/W2018990310","https://openalex.org/W2028394888","https://openalex.org/W2078039548","https://openalex.org/W2082029531","https://openalex.org/W2084052726","https://openalex.org/W2097308346","https://openalex.org/W2104266187","https://openalex.org/W2115706991","https://openalex.org/W2118103795","https://openalex.org/W2119667497","https://openalex.org/W2120504013","https://openalex.org/W2123099218","https://openalex.org/W2127271355","https://openalex.org/W2129131372","https://openalex.org/W2142683286","https://openalex.org/W2145096794","https://openalex.org/W2147800946","https://openalex.org/W2155473288","https://openalex.org/W2155893237","https://openalex.org/W2164452299","https://openalex.org/W2219727625","https://openalex.org/W2273561594","https://openalex.org/W2289917018","https://openalex.org/W2296616510","https://openalex.org/W2578009003","https://openalex.org/W2625429595","https://openalex.org/W2772147622","https://openalex.org/W2792550911","https://openalex.org/W2809635958","https://openalex.org/W2884337350","https://openalex.org/W2919115771","https://openalex.org/W2951236102","https://openalex.org/W2963676935","https://openalex.org/W2964047892","https://openalex.org/W3102661353","https://openalex.org/W3102722370","https://openalex.org/W4250955649","https://openalex.org/W6602211262","https://openalex.org/W6637242042","https://openalex.org/W6677645113"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W2378166785","https://openalex.org/W3133861977","https://openalex.org/W2156466545","https://openalex.org/W2379468505","https://openalex.org/W3104324607","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W4206981968","https://openalex.org/W2308961925"],"abstract_inverted_index":{"Deep":[0],"learning":[1,79,161],"has":[2,42],"been":[3,44,57],"exploited":[4,165],"in":[5,23,28,128,158],"compressive":[6,129],"sensing":[7],"to":[8,148,172],"reduce":[9],"the":[10,33,48,88,102,109,134,154,174,184],"computational":[11],"complexity":[12],"of":[13,35,53,87,156,177],"reconstruction":[14,19,41,80,193],"algorithms.":[15],"However,":[16],"existing":[17,188],"deep-learning-based":[18],"algorithms":[20,55,186,194],"might":[21],"result":[22],"poor":[24],"performance,":[25],"when":[26],"applied":[27],"wireless":[29,77,113],"transmission":[30,114],"environments.":[31],"First,":[32],"impact":[34,111],"channel":[36,117],"noise":[37,118],"and":[38,64,99,119,121,190],"fading":[39,120],"on":[40,137],"not":[43],"considered.":[45],"Second,":[46],"with":[47,101],"fully":[49,164],"connected":[50],"layers,":[51],"most":[52,84],"these":[54,72],"have":[56],"designed":[58,98],"for":[59,166],"a":[60,76,92],"given":[61],"sampling":[62],"ratio,":[63],"thus":[65],"cannot":[66],"handle":[67],"bandwidth":[68,122],"variations.":[69],"To":[70,107],"tackle":[71],"problems,":[73],"we":[74,169],"propose":[75,170],"deep":[78,191],"network":[81,105],"(WDLReconNet).":[82],"The":[83,179],"distinctive":[85],"aspect":[86],"WDLReconNet":[89],"is":[90,97,144],"that":[91,183],"feature":[93,160],"enhancement":[94],"layer":[95],"(FEL)":[96],"combined":[100],"convolutional":[103],"neural":[104],"(CNN).":[106],"combat":[108],"adverse":[110],"from":[112],"environments":[115],"(i.e.,":[116],"variations),":[123],"FEL":[124],"enhances":[125],"features":[126],"remained":[127],"measurements":[130],"by":[131,146],"roughly":[132],"recovering":[133],"signal":[135,143,159],"based":[136],"dictionary":[138],"learning.":[139],"Then,":[140],"this":[141,152],"rough":[142],"processed":[145],"CNN":[147,157],"finalize":[149],"reconstruction.":[150,167],"In":[151],"way,":[153],"advantages":[155],"can":[162],"be":[163],"Furthermore,":[168],"fast-WDLReconNet":[171],"accelerate":[173],"training":[175],"process":[176],"WDLReconNet.":[178],"experimental":[180],"results":[181],"demonstrate":[182],"proposed":[185],"outperform":[187],"traditional":[189],"learning-based":[192],"under":[195],"various":[196],"scenarios.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}