{"id":"https://openalex.org/W2584691341","doi":"https://doi.org/10.1109/glocom.2016.7841634","title":"Blind Signal Classification via Sparse Coding","display_name":"Blind Signal Classification via Sparse Coding","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2584691341","doi":"https://doi.org/10.1109/glocom.2016.7841634","mag":"2584691341"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2016.7841634","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2016.7841634","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://nrs.harvard.edu/urn-3:HUL.InstRepos:34390121","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012323997","display_name":"Youngjune Gwon","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Youngjune Gwon","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050202032","display_name":"Siamak Dastangoo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siamak Dastangoo","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066867900","display_name":"H. T. Kung","orcid":"https://orcid.org/0000-0002-3348-3788"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"H. T. Kung","raw_affiliation_strings":["Harvard University"],"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001325196","display_name":"Carl Fossa","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carl Fossa","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5012323997"],"corresponding_institution_ids":["https://openalex.org/I4210122954"],"apc_list":null,"apc_paid":null,"fwci":0.8868,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85658997,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"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":0.9995999932289124,"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":0.9995999932289124,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7321731448173523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6830116510391235},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.678527295589447},{"id":"https://openalex.org/keywords/matching-pursuit","display_name":"Matching pursuit","score":0.5757832527160645},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.5681890845298767},{"id":"https://openalex.org/keywords/matched-filter","display_name":"Matched filter","score":0.5178449749946594},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5038477778434753},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.4927807152271271},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4423428773880005},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.43117576837539673},{"id":"https://openalex.org/keywords/convolutional-code","display_name":"Convolutional code","score":0.42647600173950195},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.42413339018821716},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.41723740100860596},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.21984609961509705},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.2036539912223816},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.16807183623313904},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13188010454177856},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08208781480789185}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7321731448173523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6830116510391235},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.678527295589447},{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.5757832527160645},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.5681890845298767},{"id":"https://openalex.org/C50151734","wikidata":"https://www.wikidata.org/wiki/Q1759577","display_name":"Matched filter","level":3,"score":0.5178449749946594},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5038477778434753},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.4927807152271271},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4423428773880005},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.43117576837539673},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.42647600173950195},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.42413339018821716},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.41723740100860596},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.21984609961509705},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.2036539912223816},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.16807183623313904},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13188010454177856},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08208781480789185},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/glocom.2016.7841634","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2016.7841634","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"},{"id":"pmh:oai:dash.harvard.edu:1/34390121","is_oa":true,"landing_page_url":"http://nrs.harvard.edu/urn-3:HUL.InstRepos:34390121","pdf_url":null,"source":{"id":"https://openalex.org/S4306401540","display_name":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I136199984","host_organization_name":"Harvard University","host_organization_lineage":["https://openalex.org/I136199984"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":{"id":"pmh:oai:dash.harvard.edu:1/34390121","is_oa":true,"landing_page_url":"http://nrs.harvard.edu/urn-3:HUL.InstRepos:34390121","pdf_url":null,"source":{"id":"https://openalex.org/S4306401540","display_name":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I136199984","host_organization_name":"Harvard University","host_organization_lineage":["https://openalex.org/I136199984"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W55761002","https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1963547452","https://openalex.org/W1986931325","https://openalex.org/W2021302824","https://openalex.org/W2063978378","https://openalex.org/W2101018825","https://openalex.org/W2102380305","https://openalex.org/W2104908641","https://openalex.org/W2129131372","https://openalex.org/W2129812935","https://openalex.org/W2131241154","https://openalex.org/W2131904035","https://openalex.org/W2132211083","https://openalex.org/W2135046866","https://openalex.org/W2140571193","https://openalex.org/W2144354855","https://openalex.org/W2145889472","https://openalex.org/W2149194912","https://openalex.org/W2153620960","https://openalex.org/W2156288624","https://openalex.org/W2156909104","https://openalex.org/W2159050893","https://openalex.org/W2160547390","https://openalex.org/W2162931300","https://openalex.org/W2163922914","https://openalex.org/W2999061182","https://openalex.org/W4233558979","https://openalex.org/W6679450940","https://openalex.org/W6679539681","https://openalex.org/W6682279845","https://openalex.org/W6682776334","https://openalex.org/W6683965311"],"related_works":["https://openalex.org/W2345184372","https://openalex.org/W3013515612","https://openalex.org/W2136184105","https://openalex.org/W2187500075","https://openalex.org/W2041399278","https://openalex.org/W2160451891","https://openalex.org/W2336974148","https://openalex.org/W2056016498","https://openalex.org/W2389470892","https://openalex.org/W4293087713"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,24,85,160,171,186],"novel":[3],"RF":[4,82,111,115],"signal":[5,37,63,93],"classification":[6,94,183],"method":[7,15,127],"based":[8],"on":[9],"sparse":[10,26,71],"coding,":[11],"an":[12,34,43,58,109],"unsupervised":[13,59],"learning":[14],"popular":[16],"in":[17,57,108,130],"computer":[18],"vision.":[19],"In":[20],"particular,":[21],"we":[22,149,179],"employ":[23],"convolutional":[25],"coder":[27],"that":[28,95,148],"can":[29,51,90,150],"extract":[30],"high-level":[31],"features":[32],"of":[33,46,155],"unknown":[35],"received":[36],"by":[38],"maximal":[39],"similarity":[40,132],"matching":[41],"against":[42,133],"over-complete":[44],"dictionary":[45,50,135],"filter":[47,124],"patterns.":[48],"Such":[49],"be":[52],"either":[53],"generated":[54],"or":[55],"learned":[56],"fashion":[60],"from":[61],"measured":[62],"examples":[64],"conveying":[65],"no":[66,97],"ground-truth":[67],"labels.":[68],"The":[69],"computed":[70],"code":[72],"is":[73],"then":[74],"applied":[75],"to":[76,120,185],"train":[77],"SVM":[78],"classifiers":[79],"for":[80,142,175],"discriminating":[81,140],"signals.":[83],"As":[84],"result,":[86],"the":[87,105,122,134,138,182],"proposed":[88],"approach":[89],"achieve":[91],"blind":[92],"requires":[96],"prior":[98],"knowledge":[99],"(e.g.,":[100],"MCS,":[101],"pulse":[102,118],"shaping)":[103],"about":[104],"signals":[106,116,158],"present":[107],"arbitrary":[110],"channel.":[112],"Since":[113],"modulated":[114,157],"undergo":[117],"shaping":[119],"aid":[121],"matched":[123],"detection,":[125],"our":[126],"exploits":[128],"variability":[129],"relative":[131],"atoms":[136],"as":[137],"key":[139],"factor":[141],"classification.":[143],"Our":[144],"experimental":[145],"results":[146],"indicate":[147],"blindly":[151],"separate":[152],"different":[153],"classes":[154],"digitally":[156],"with":[159],"0.703":[161],"recall":[162,188],"and":[163,189],"0.246":[164],"false":[165,191],"alarm":[166],"at":[167],"20dB":[168],"SNR.":[169],"Provided":[170],"small":[172],"labeled":[173],"dataset":[174],"supervised":[176],"classifier":[177],"training,":[178],"could":[180],"improve":[181],"performance":[184],"0.878":[187],"0.141":[190],"alarm.":[192]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}