{"id":"https://openalex.org/W4415550270","doi":"https://doi.org/10.1109/tvt.2025.3586859","title":"Deep Learning Based Channel Estimation for Deep-Space Communications","display_name":"Deep Learning Based Channel Estimation for Deep-Space Communications","publication_year":2025,"publication_date":"2025-07-08","ids":{"openalex":"https://openalex.org/W4415550270","doi":"https://doi.org/10.1109/tvt.2025.3586859"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2025.3586859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3586859","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Transactions on Vehicular Technology","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/A5047036838","display_name":"Lianning Cai","orcid":"https://orcid.org/0000-0003-1369-8877"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lianning Cai","raw_affiliation_strings":["School of Communication and Electronic Engineering, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Communication and Electronic Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090837003","display_name":"Guanjun Xu","orcid":"https://orcid.org/0000-0002-7788-0208"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanjun Xu","raw_affiliation_strings":["Space Information Research Institute and Zhejiang Key Laboratory of Space Information Sensing and Transmission, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Space Information Research Institute and Zhejiang Key Laboratory of Space Information Sensing and Transmission, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071725895","display_name":"Qinyu Zhang","orcid":"https://orcid.org/0000-0001-9272-0475"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinyu Zhang","raw_affiliation_strings":["Communication Engineering Research Center, Harbin Institute of Technology Shenzhen, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Communication Engineering Research Center, Harbin Institute of Technology Shenzhen, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhaohui Song","orcid":"https://orcid.org/0000-0002-8583-6768"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohui Song","raw_affiliation_strings":["Space Information Research Institute and Zhejiang Key Laboratory of Space Information Sensing and Transmission, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Space Information Research Institute and Zhejiang Key Laboratory of Space Information Sensing and Transmission, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100653327","display_name":"Wei Zhang","orcid":"https://orcid.org/0000-0002-1059-3642"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia","School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]},{"raw_affiliation_string":"School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5047036838"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":8.768,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.97811289,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"74","issue":"12","first_page":"19743","last_page":"19748"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12450","display_name":"Radio Astronomy Observations and Technology","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12450","display_name":"Radio Astronomy Observations and Technology","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11946","display_name":"Antenna Design and Optimization","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T12042","display_name":"Satellite Communication Systems","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/fading","display_name":"Fading","score":0.6438999772071838},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6200000047683716},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5254999995231628},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5077000260353088},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4228000044822693},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.41280001401901245},{"id":"https://openalex.org/keywords/doppler-effect","display_name":"Doppler effect","score":0.40689998865127563},{"id":"https://openalex.org/keywords/bit-error-rate","display_name":"Bit error rate","score":0.3783000111579895},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.37459999322891235}],"concepts":[{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.6438999772071838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6340000033378601},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6200000047683716},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5254999995231628},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.49380001425743103},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4374000132083893},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4228000044822693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42160001397132874},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C142757262","wikidata":"https://www.wikidata.org/wiki/Q76436","display_name":"Doppler effect","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C56296756","wikidata":"https://www.wikidata.org/wiki/Q840922","display_name":"Bit error rate","level":3,"score":0.3783000111579895},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.37459999322891235},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.36800000071525574},{"id":"https://openalex.org/C101765175","wikidata":"https://www.wikidata.org/wiki/Q577764","display_name":"Communications system","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C102637530","wikidata":"https://www.wikidata.org/wiki/Q3078889","display_name":"Scintillation","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.28999999165534973},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.27889999747276306},{"id":"https://openalex.org/C40409654","wikidata":"https://www.wikidata.org/wiki/Q375889","display_name":"Orthogonal frequency-division multiplexing","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C187107819","wikidata":"https://www.wikidata.org/wiki/Q835696","display_name":"NASA Deep Space Network","level":3,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2025.3586859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3586859","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4343183693","display_name":null,"funder_award_id":"62027802","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6745607870","display_name":null,"funder_award_id":"62271202","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2007375052","https://openalex.org/W2072201824","https://openalex.org/W2154068430","https://openalex.org/W2159639016","https://openalex.org/W2976103643","https://openalex.org/W3112949451","https://openalex.org/W3153008557","https://openalex.org/W3165573226","https://openalex.org/W3188870758","https://openalex.org/W4210813131","https://openalex.org/W4214587880","https://openalex.org/W4385517042","https://openalex.org/W4389352475","https://openalex.org/W4402350860","https://openalex.org/W4403790717"],"related_works":[],"abstract_inverted_index":{"During":[0],"the":[1,7,26,41,47,74,90,99,117,135],"period":[2],"of":[3,28,76,92],"superior":[4],"solar":[5,12,132],"conjunction,":[6],"deep-space":[8,136],"channel":[9,32,50,59,93],"suffers":[10],"from":[11],"scintillation":[13,133],"and":[14,55,82],"large":[15],"Doppler":[16,43],"shifts,":[17],"leading":[18],"to":[19,45,105,131],"highly":[20],"time-varying":[21],"communication":[22],"links.":[23],"To":[24],"guarantee":[25],"quality":[27],"data":[29],"transmission,":[30],"accurate":[31],"estimation":[33,60],"is":[34],"indispensable.":[35],"In":[36,126],"this":[37],"paper,":[38],"we":[39],"use":[40],"Gaussian":[42],"spectrum":[44],"model":[46],"time-selective":[48],"fading":[49],"in":[51,109,134],"deep":[52],"space":[53],"communications":[54],"propose":[56],"a":[57,64],"data-driven":[58],"framework":[61],"based":[62],"on":[63],"convolutional":[65],"neural":[66],"network-long":[67],"short-term":[68],"memory":[69],"(CNN-LSTM)":[70],"model.":[71],"We":[72],"leverage":[73],"strength":[75],"CNN":[77],"for":[78,84],"efficient":[79],"feature":[80],"extraction":[81],"LSTM":[83],"modeling":[85],"temporal":[86],"dependencies,":[87],"further":[88],"enhancing":[89],"performance":[91,115],"estimation.":[94],"Simulation":[95],"results":[96],"show":[97],"that":[98],"proposed":[100],"CNN-LSTM":[101],"method":[102],"achieves":[103],"up":[104],"5":[106],"dB":[107],"improvement":[108],"normalized":[110],"mean":[111,121],"square":[112,122],"error":[113,123],"(NMSE)":[114],"over":[116],"conventional":[118],"linear":[119],"minimum":[120],"(LMMSE)":[124],"method.":[125],"addition,":[127],"it":[128],"exhibits":[129],"robustness":[130],"channel.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":8}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-26T00:00:00"}
