{"id":"https://openalex.org/W3125909662","doi":"https://doi.org/10.1109/globecom42002.2020.9322249","title":"DeepIoT: Deep Learning Based Symbol Detection for Spatially Undersampled Internet of Things","display_name":"DeepIoT: Deep Learning Based Symbol Detection for Spatially Undersampled Internet of Things","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3125909662","doi":"https://doi.org/10.1109/globecom42002.2020.9322249","mag":"3125909662"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9322249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322249","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","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/A5023496945","display_name":"Zhe Ma","orcid":"https://orcid.org/0000-0001-6805-2139"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhe Ma","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086877493","display_name":"Mengnan Jian","orcid":"https://orcid.org/0000-0002-6750-5690"},"institutions":[{"id":"https://openalex.org/I4210098582","display_name":"ZTE (China)","ror":"https://ror.org/00rjhhq63","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210098582"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengnan Jian","raw_affiliation_strings":["Wireless Product R&D Institute, ZTE corporation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Wireless Product R&D Institute, ZTE corporation, Beijing, China","institution_ids":["https://openalex.org/I4210098582"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050402661","display_name":"Feifei Gao","orcid":"https://orcid.org/0000-0001-8896-352X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feifei Gao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100773343","display_name":"Xuemin Shen","orcid":"https://orcid.org/0000-0002-4140-287X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xuemin Sherman Shen","raw_affiliation_strings":["University of Waterloo, Waterloo, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023496945"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57855694,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T11458","display_name":"Advanced Wireless Communication Technologies","score":0.9980000257492065,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9962000250816345,"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":0.7962647676467896},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.667823076248169},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.6531385779380798},{"id":"https://openalex.org/keywords/symbol","display_name":"Symbol (formal)","score":0.5973516702651978},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5950638055801392},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.5679754614830017},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5230711102485657},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4466113746166229},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4161553382873535},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3906889855861664},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3555869460105896},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.1956949532032013},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1924409568309784}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7962647676467896},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.667823076248169},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.6531385779380798},{"id":"https://openalex.org/C134400042","wikidata":"https://www.wikidata.org/wiki/Q2372244","display_name":"Symbol (formal)","level":2,"score":0.5973516702651978},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5950638055801392},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.5679754614830017},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5230711102485657},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4466113746166229},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4161553382873535},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3906889855861664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3555869460105896},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.1956949532032013},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1924409568309784},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom42002.2020.9322249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322249","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7699133730","display_name":null,"funder_award_id":"4182030,L182042","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1509061701","https://openalex.org/W1971733720","https://openalex.org/W2010575773","https://openalex.org/W2015606516","https://openalex.org/W2058401212","https://openalex.org/W2132417948","https://openalex.org/W2292837623","https://openalex.org/W2295559644","https://openalex.org/W2706056020","https://openalex.org/W2727556029","https://openalex.org/W2734408173","https://openalex.org/W2735057770","https://openalex.org/W2804119529","https://openalex.org/W2897275976","https://openalex.org/W2962956060","https://openalex.org/W2963889719","https://openalex.org/W2970898848","https://openalex.org/W3006643426","https://openalex.org/W3009389709","https://openalex.org/W4256217385","https://openalex.org/W6751996035"],"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/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000"],"abstract_inverted_index":{"With":[0],"the":[1,5,43,47,55,63,71,85,123,152,164,171,190],"explosive":[2],"growth":[3],"of":[4,7,13,24,65,73,126,154],"Internet":[6],"Things":[8],"(IoT),":[9],"a":[10,22,108,137,145,181],"massive":[11,44,51],"number":[12,64,72],"IoT":[14,56,89,129],"devices":[15,66,156],"are":[16,34,100],"deployed":[17],"so":[18],"as":[19],"to":[20,58,81,97,102,117,149,189],"realize":[21],"variety":[23],"advanced":[25],"applications,":[26,41],"i.e.,":[27],"environmental":[28],"monitoring":[29],"and":[30,46,79,157,178],"smart":[31],"traffic.":[32],"There":[33],"two":[35],"main":[36],"characteristics":[37],"in":[38,88,107],"these":[39],"typical":[40],"namely":[42],"connectivity":[45,52],"sporadic":[48,86],"transmission.":[49],"The":[50],"usually":[53],"leads":[54],"system":[57],"be":[59],"spatially":[60,109,127],"undersampled,":[61],"since":[62],"is":[67],"much":[68],"larger":[69],"than":[70],"receiver":[74],"antennas,":[75],"which":[76,98],"brings":[77],"difficulties":[78],"challenges":[80],"symbol":[82,104,124],"detection.":[83],"Fortunately,":[84],"transmission":[87],"communication":[90],"introduces":[91],"sparsity":[92],"into":[93,122],"transmitted":[94],"symbols,":[95],"thanks":[96],"we":[99,115,135],"able":[101],"perform":[103],"detection":[105,125],"even":[106],"undersampled":[110,128],"scenario.":[111],"In":[112],"this":[113],"paper,":[114],"attempt":[116],"incorporate":[118],"deep":[119],"learning":[120],"(DL)":[121],"with":[130],"sporadically":[131],"transmitting":[132,159],"devices.":[133],"Specifically,":[134],"propose":[136],"novel":[138],"DL-based":[139],"detector":[140],"named":[141],"DeepIoT":[142,172],"that":[143,170],"employs":[144],"variant":[146],"autoencoder":[147],"network":[148],"recover":[150],"both":[151],"indices":[153],"active":[155],"their":[158],"symbols":[160],"by":[161],"using":[162],"only":[163,180],"received":[165],"signal.":[166],"Simulation":[167],"results":[168],"show":[169],"can":[173],"outperform":[174],"various":[175],"existing":[176],"methods":[177],"has":[179],"1.5-":[182],"2dB":[183],"signal-to-noise":[184],"ratio":[185],"(SNR)":[186],"loss":[187],"compared":[188],"optimal":[191],"maximum":[192],"likelihood":[193],"(ML)":[194],"detector.":[195]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
