{"id":"https://openalex.org/W2794495134","doi":"https://doi.org/10.1109/comsnets.2018.8328192","title":"Autocorrelation-based spectrum sensing of FBMC signal","display_name":"Autocorrelation-based spectrum sensing of FBMC signal","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2794495134","doi":"https://doi.org/10.1109/comsnets.2018.8328192","mag":"2794495134"},"language":"en","primary_location":{"id":"doi:10.1109/comsnets.2018.8328192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets.2018.8328192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 10th International Conference on Communication Systems &amp; Networks (COMSNETS)","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/A5012068060","display_name":"Upender Keesara","orcid":null},"institutions":[{"id":"https://openalex.org/I64189192","display_name":"International Institute of Information Technology, Hyderabad","ror":"https://ror.org/05f11g639","country_code":"IN","type":"education","lineage":["https://openalex.org/I64189192"]},{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]}],"countries":["IN","US"],"is_corresponding":true,"raw_author_name":"Upender Keesara","raw_affiliation_strings":["Signal Processing and Communication Research Center, International Institute of Information Technology, Hyderabad, INDIA"],"affiliations":[{"raw_affiliation_string":"Signal Processing and Communication Research Center, International Institute of Information Technology, Hyderabad, INDIA","institution_ids":["https://openalex.org/I64189192","https://openalex.org/I4210121626"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063390942","display_name":"Sachin Chaudhari","orcid":"https://orcid.org/0000-0003-1923-0925"},"institutions":[{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]},{"id":"https://openalex.org/I64189192","display_name":"International Institute of Information Technology, Hyderabad","ror":"https://ror.org/05f11g639","country_code":"IN","type":"education","lineage":["https://openalex.org/I64189192"]}],"countries":["IN","US"],"is_corresponding":false,"raw_author_name":"Sachin Chaudhari","raw_affiliation_strings":["Signal Processing and Communication Research Center, International Institute of Information Technology, Hyderabad, INDIA"],"affiliations":[{"raw_affiliation_string":"Signal Processing and Communication Research Center, International Institute of Information Technology, Hyderabad, INDIA","institution_ids":["https://openalex.org/I64189192","https://openalex.org/I4210121626"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5012068060"],"corresponding_institution_ids":["https://openalex.org/I4210121626","https://openalex.org/I64189192"],"apc_list":null,"apc_paid":null,"fwci":0.1288,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45334079,"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":"151","last_page":"158"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11873","display_name":"PAPR reduction in OFDM","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11873","display_name":"PAPR reduction in OFDM","score":1.0,"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/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.9945999979972839,"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/T10575","display_name":"Wireless Communication Networks Research","score":0.9932000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.7424437999725342},{"id":"https://openalex.org/keywords/filter-bank","display_name":"Filter bank","score":0.6760240793228149},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.6727307438850403},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.6127333641052246},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5315513610839844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5234436392784119},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5032112002372742},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.49430084228515625},{"id":"https://openalex.org/keywords/matched-filter","display_name":"Matched filter","score":0.4896850883960724},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.4590093493461609},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.41997990012168884},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41326993703842163},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.4123954176902771},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.38255178928375244},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.36091744899749756},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31005650758743286},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.29578346014022827},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29504621028900146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28163963556289673},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16666626930236816},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07181930541992188}],"concepts":[{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.7424437999725342},{"id":"https://openalex.org/C100515483","wikidata":"https://www.wikidata.org/wiki/Q3268235","display_name":"Filter bank","level":3,"score":0.6760240793228149},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.6727307438850403},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.6127333641052246},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5315513610839844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5234436392784119},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5032112002372742},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.49430084228515625},{"id":"https://openalex.org/C50151734","wikidata":"https://www.wikidata.org/wiki/Q1759577","display_name":"Matched filter","level":3,"score":0.4896850883960724},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.4590093493461609},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.41997990012168884},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41326993703842163},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.4123954176902771},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.38255178928375244},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.36091744899749756},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31005650758743286},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.29578346014022827},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29504621028900146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28163963556289673},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16666626930236816},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07181930541992188},{"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/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"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/comsnets.2018.8328192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets.2018.8328192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 10th International Conference on Communication Systems &amp; Networks (COMSNETS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W595424452","https://openalex.org/W1964793896","https://openalex.org/W2093444176","https://openalex.org/W2103970559","https://openalex.org/W2134673099","https://openalex.org/W2143186091","https://openalex.org/W2168890544","https://openalex.org/W2172139273","https://openalex.org/W2597484136","https://openalex.org/W2604480971","https://openalex.org/W2617165301"],"related_works":["https://openalex.org/W2012656072","https://openalex.org/W1911817635","https://openalex.org/W2074565401","https://openalex.org/W2810224748","https://openalex.org/W2011915004","https://openalex.org/W2353656796","https://openalex.org/W1607924090","https://openalex.org/W1967187784","https://openalex.org/W2565587144","https://openalex.org/W2242778450"],"abstract_inverted_index":{"The":[0,97],"focus":[1],"of":[2,51,62,100,123,127,140,157,164],"this":[3,20,82,109],"paper":[4],"is":[5,40,89,104,131],"on":[6],"a":[7],"feature":[8,87],"detector":[9,88,103,110,130,143],"for":[10],"filter":[11],"bank":[12],"multicarrier":[13],"(FBMC)":[14],"signal":[15,28,39,53,94,115],"in":[16,95],"cognitive":[17],"radio.":[18],"In":[19],"paper,":[21],"we":[22],"first":[23],"prove":[24],"that":[25,137],"the":[26,37,47,57,65,75,101,121,124,128,138,141,155,162,165],"FBMC":[27,38,52,93,114],"samples":[29,73],"are":[30],"uncorrelated":[31],"with":[32],"each":[33],"other.":[34],"However,":[35],"if":[36],"processed":[41],"by":[42],"our":[43],"proposed":[44,77,90,102,129,166],"method,":[45],"then":[46],"autocorrelation":[48,85],"function":[49],"(ACF)":[50],"becomes":[54],"non-zero":[55],"at":[56],"lag":[58],"equal":[59],"to":[60,91,147],"number":[61],"subcarriers.":[63],"On":[64],"other":[66],"hand,":[67],"additive":[68],"white":[69],"Gaussian":[70],"noise":[71,117],"(AWGN)":[72],"after":[74],"same":[76],"processing":[78],"remain":[79],"uncorrelated.":[80],"Using":[81],"feature,":[83],"an":[84],"based":[86],"detect":[92],"noise.":[96],"main":[98],"advantage":[99],"that,":[105],"unlike":[106],"blind":[107],"detectors,":[108],"can":[111,144],"distinguish":[112],"between":[113],"and":[116],"(or":[118],"interference).":[119],"Next,":[120],"distribution":[122],"test":[125],"statistic":[126],"derived":[132],"under":[133],"noise-only":[134],"scenario":[135],"so":[136],"threshold":[139],"Neyman-Pearson":[142],"be":[145],"designed":[146],"maintain":[148],"constant":[149],"false":[150],"alarm":[151],"rate":[152],"while":[153],"maximizing":[154],"probability":[156],"detection.":[158],"Simulation":[159],"results":[160],"demonstrate":[161],"efficacy":[163],"detector.":[167]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
