{"id":"https://openalex.org/W3008727209","doi":"https://doi.org/10.1109/lwc.2020.2976992","title":"Grant-Free NOMA with Device Activity Learning Using Long Short-Term Memory","display_name":"Grant-Free NOMA with Device Activity Learning Using Long Short-Term Memory","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3008727209","doi":"https://doi.org/10.1109/lwc.2020.2976992","mag":"3008727209"},"language":"en","primary_location":{"id":"doi:10.1109/lwc.2020.2976992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lwc.2020.2976992","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/A5015206310","display_name":"Xiaqing Miao","orcid":"https://orcid.org/0000-0001-5503-6578"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaqing Miao","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081342685","display_name":"Dongning Guo","orcid":"https://orcid.org/0000-0001-9000-3480"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongning Guo","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100771966","display_name":"Xiangming Li","orcid":"https://orcid.org/0000-0003-3128-6219"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangming Li","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5015206310"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":5.1057,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.96868064,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11458","display_name":"Advanced Wireless Communication Technologies","score":0.9998999834060669,"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":0.9990000128746033,"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/noma","display_name":"Noma","score":0.940564751625061},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.815231442451477},{"id":"https://openalex.org/keywords/telecommunications-link","display_name":"Telecommunications link","score":0.6526153087615967},{"id":"https://openalex.org/keywords/matching-pursuit","display_name":"Matching pursuit","score":0.598910927772522},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5502817034721375},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5489878058433533},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.510370671749115},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.47285130620002747},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46699658036231995},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4435168504714966},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42319706082344055},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4144498109817505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38884690403938293},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1783154308795929},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16877663135528564},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1286410689353943},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.10265564918518066},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07972109317779541}],"concepts":[{"id":"https://openalex.org/C2775918612","wikidata":"https://www.wikidata.org/wiki/Q994794","display_name":"Noma","level":3,"score":0.940564751625061},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.815231442451477},{"id":"https://openalex.org/C138660444","wikidata":"https://www.wikidata.org/wiki/Q5607897","display_name":"Telecommunications link","level":2,"score":0.6526153087615967},{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.598910927772522},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5502817034721375},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5489878058433533},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.510370671749115},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.47285130620002747},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46699658036231995},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4435168504714966},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42319706082344055},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4144498109817505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38884690403938293},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1783154308795929},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16877663135528564},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1286410689353943},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.10265564918518066},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07972109317779541},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lwc.2020.2976992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lwc.2020.2976992","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1922937245","https://openalex.org/W2064675550","https://openalex.org/W2078804453","https://openalex.org/W2114103671","https://openalex.org/W2125838225","https://openalex.org/W2127271355","https://openalex.org/W2136696369","https://openalex.org/W2160979406","https://openalex.org/W2289917018","https://openalex.org/W2344857408","https://openalex.org/W2508031406","https://openalex.org/W2548516812","https://openalex.org/W2735475593","https://openalex.org/W2883289908","https://openalex.org/W2900452440","https://openalex.org/W2913340256"],"related_works":["https://openalex.org/W2551136801","https://openalex.org/W3083878955","https://openalex.org/W4320921142","https://openalex.org/W3209093869","https://openalex.org/W2039122851","https://openalex.org/W2380403159","https://openalex.org/W2460295244","https://openalex.org/W3208653488","https://openalex.org/W4286610606","https://openalex.org/W4385800891"],"abstract_inverted_index":{"Non-orthogonal":[0],"multiple":[1],"access":[2],"(NOMA)":[3],"is":[4,21,57,104],"a":[5,53,82],"promising":[6],"technique":[7],"for":[8,89],"future":[9],"cellular":[10],"networks.":[11],"A":[12],"major":[13],"challenge":[14],"in":[15,64],"the":[16,35,40,61,65,70,101,120],"uplink":[17],"of":[18,37,43],"grant-free":[19],"NOMA":[20],"to":[22,30,47,59,81,106],"identify":[23],"all":[24],"active":[25],"devices":[26,44],"as":[27,29,78,111],"well":[28],"decode":[31],"their":[32],"data.":[33,72],"In":[34,50],"Internet":[36],"Things":[38],"(IoT),":[39],"on-off":[41],"activities":[42,63],"are":[45,76],"predictable":[46],"various":[48],"degrees.":[49],"this":[51],"letter,":[52],"deep":[54],"learning":[55],"algorithm":[56,88],"employed":[58],"predict":[60],"device":[62,91],"current":[66],"slot":[67],"by":[68],"exploiting":[69],"history":[71],"The":[73],"prediction":[74],"results":[75,98],"applied":[77],"input":[79],"priors":[80],"modified":[83],"orthogonal":[84],"matching":[85],"pursuit":[86],"(OMP)":[87],"joint":[90],"identification":[92],"and":[93],"data":[94],"detection.":[95],"Numerical":[96],"simulation":[97],"demonstrate":[99],"that":[100],"error":[102],"rate":[103],"reduced":[105],"at":[107,119],"least":[108],"ten":[109],"times":[110],"compared":[112],"with":[113],"conventional":[114],"compressed":[115],"sensing":[116],"based":[117],"algorithms":[118],"same":[121],"signal-to-noise":[122],"ratio.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}