{"id":"https://openalex.org/W2071184010","doi":"https://doi.org/10.1109/twc.2014.2359019","title":"Distributed Maximum Likelihood Classification of Linear Modulations Over Nonidentical Flat Block-Fading Gaussian Channels","display_name":"Distributed Maximum Likelihood Classification of Linear Modulations Over Nonidentical Flat Block-Fading Gaussian Channels","publication_year":2014,"publication_date":"2014-09-18","ids":{"openalex":"https://openalex.org/W2071184010","doi":"https://doi.org/10.1109/twc.2014.2359019","mag":"2071184010"},"language":"en","primary_location":{"id":"doi:10.1109/twc.2014.2359019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/twc.2014.2359019","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"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 Wireless Communications","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/A5026166103","display_name":"Berkan D\u00fclek","orcid":"https://orcid.org/0000-0003-2126-7356"},"institutions":[{"id":"https://openalex.org/I66514158","display_name":"Hacettepe University","ror":"https://ror.org/04kwvgz42","country_code":"TR","type":"education","lineage":["https://openalex.org/I66514158"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Berkan Dulek","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Hacettepe University, Ankara, Turkey","Dept. of Electr. & Electron. Eng., Hacettepe Univ., Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Hacettepe University, Ankara, Turkey","institution_ids":["https://openalex.org/I66514158"]},{"raw_affiliation_string":"Dept. of Electr. & Electron. Eng., Hacettepe Univ., Ankara, Turkey","institution_ids":["https://openalex.org/I66514158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101674748","display_name":"Onur \u00d6zdemir","orcid":"https://orcid.org/0000-0003-3104-2804"},"institutions":[{"id":"https://openalex.org/I4210151392","display_name":"Boston Fusion (United States)","ror":"https://ror.org/05ayy0v22","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Onur Ozdemir","raw_affiliation_strings":["Boston Fusion Corp., Andro Computational Solutions, Burlington, MA, USA","Boston Fusion Corp., Burlington, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston Fusion Corp., Andro Computational Solutions, Burlington, MA, USA","institution_ids":["https://openalex.org/I4210151392"]},{"raw_affiliation_string":"Boston Fusion Corp., Burlington, MA, USA","institution_ids":["https://openalex.org/I4210151392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018292481","display_name":"Pramod K. Varshney","orcid":"https://orcid.org/0000-0003-4504-5088"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pramod K. Varshney","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA","[Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]},{"raw_affiliation_string":"[Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA]","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100755437","display_name":"Wei Su","orcid":"https://orcid.org/0000-0003-0952-0398"},"institutions":[{"id":"https://openalex.org/I1304082316","display_name":"United States Department of the Army","ror":"https://ror.org/035w1gb98","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796"]},{"id":"https://openalex.org/I2800475051","display_name":"CECOM Software Engineering Center","ror":"https://ror.org/044webc45","country_code":"US","type":"facility","lineage":["https://openalex.org/I2800475051","https://openalex.org/I4405253748","https://openalex.org/I4405254906"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Su","raw_affiliation_strings":["Army CERDEC, Aberdeen Proving Ground, MD, USA","Dev. & Eng. Center, Army Commun.-Electron. Res., Aberdeen Proving Ground, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Army CERDEC, Aberdeen Proving Ground, MD, USA","institution_ids":["https://openalex.org/I2800475051"]},{"raw_affiliation_string":"Dev. & Eng. Center, Army Commun.-Electron. Res., Aberdeen Proving Ground, MD, USA","institution_ids":["https://openalex.org/I1304082316"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.8062,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.93893258,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"14","issue":"2","first_page":"724","last_page":"737"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9997000098228455,"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":0.9997000098228455,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fading","display_name":"Fading","score":0.6684272289276123},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5425899028778076},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.533807098865509},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.530813455581665},{"id":"https://openalex.org/keywords/matched-filter","display_name":"Matched filter","score":0.49208348989486694},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.47403740882873535},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4436514973640442},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4014279246330261},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3927878141403198},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.3737325072288513},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.34867048263549805},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.34623104333877563},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.308623731136322},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.08579382300376892}],"concepts":[{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.6684272289276123},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5425899028778076},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.533807098865509},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.530813455581665},{"id":"https://openalex.org/C50151734","wikidata":"https://www.wikidata.org/wiki/Q1759577","display_name":"Matched filter","level":3,"score":0.49208348989486694},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.47403740882873535},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4436514973640442},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4014279246330261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3927878141403198},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.3737325072288513},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.34867048263549805},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.34623104333877563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.308623731136322},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.08579382300376892},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/twc.2014.2359019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/twc.2014.2359019","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"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 Wireless Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W595252221","https://openalex.org/W654636658","https://openalex.org/W1511751337","https://openalex.org/W1520508279","https://openalex.org/W1525038591","https://openalex.org/W1579271636","https://openalex.org/W2005935422","https://openalex.org/W2005956500","https://openalex.org/W2015245929","https://openalex.org/W2017137572","https://openalex.org/W2022701556","https://openalex.org/W2049633694","https://openalex.org/W2053742104","https://openalex.org/W2064056918","https://openalex.org/W2093539031","https://openalex.org/W2098863504","https://openalex.org/W2104556565","https://openalex.org/W2104924323","https://openalex.org/W2107396783","https://openalex.org/W2114267371","https://openalex.org/W2120019163","https://openalex.org/W2121150059","https://openalex.org/W2125242600","https://openalex.org/W2142363115","https://openalex.org/W2144052787","https://openalex.org/W2147735901","https://openalex.org/W2159391495","https://openalex.org/W2160643434","https://openalex.org/W2163599171","https://openalex.org/W2170778725","https://openalex.org/W2477794859","https://openalex.org/W2478708596","https://openalex.org/W2488678869","https://openalex.org/W2501236262","https://openalex.org/W2610335499","https://openalex.org/W3125855424","https://openalex.org/W3133603318","https://openalex.org/W3148392858","https://openalex.org/W4246275837","https://openalex.org/W4246784033","https://openalex.org/W6666423226","https://openalex.org/W6674727558"],"related_works":["https://openalex.org/W1978153144","https://openalex.org/W2135359550","https://openalex.org/W102848802","https://openalex.org/W2982058819","https://openalex.org/W2130734797","https://openalex.org/W2172249169","https://openalex.org/W3176361882","https://openalex.org/W2122958477","https://openalex.org/W2025556230","https://openalex.org/W2150061385"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"consider":[4],"distributed":[5,68,151],"maximum":[6],"likelihood":[7],"(ML)":[8],"classification":[9,92,122,158],"of":[10,23,36,48,58,72,99,109,142,162,176],"digital":[11],"amplitude-phase":[12],"modulated":[13],"signals":[14],"using":[15],"multiple":[16],"sensors":[17,65,101],"that":[18,148,161],"observe":[19],"the":[20,37,45,49,55,59,64,87,100,103,110,113,121,126,132,136,149,156,169],"same":[21,157],"sequence":[22],"unknown":[24,50],"symbol":[25],"transmissions":[26],"over":[27],"nonidentical":[28],"flat":[29],"blockfading":[30],"Gaussian":[31],"noise":[32],"channels.":[33],"A":[34],"variant":[35],"expectation-maximization":[38],"(EM)":[39],"algorithm":[40],"is":[41,79,95,105],"employed":[42],"to":[43,138],"obtain":[44],"ML":[46],"estimates":[47],"channel":[51],"parameters":[52],"and":[53,90],"compute":[54],"global":[56],"log-likelihood":[57,115],"observations":[60],"received":[61],"by":[62,70,124],"all":[63,82,168],"in":[66,86,102,107,131],"a":[67,91,139,163,173],"manner":[69],"means":[71],"an":[73],"average":[74],"consensus":[75],"filter.":[76],"This":[77],"procedure":[78],"repeated":[80],"for":[81,172],"candidate":[83],"modulation":[84,111],"formats":[85],"reference":[88],"library,":[89],"decision,":[93],"which":[94,166],"available":[96],"at":[97],"any":[98],"network,":[104],"declared":[106],"favor":[108],"with":[112],"highest":[114],"score.":[116],"The":[117],"proposed":[118,150],"scheme":[119],"improves":[120],"accuracy":[123],"exploiting":[125],"signal-to-noise":[127],"ratio":[128],"(SNR)":[129],"diversity":[130],"network":[133],"while":[134],"restricting":[135],"communication":[137],"small":[140],"neighborhood":[141],"each":[143],"sensor.":[144],"Numerical":[145],"examples":[146],"show":[147],"EM-based":[152],"classifier":[153],"can":[154],"achieve":[155],"performance":[159],"as":[160],"centralized":[164],"classifier,":[165],"has":[167],"sensor":[170],"measurements,":[171],"wide":[174],"range":[175],"SNR":[177],"values.":[178]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
