{"id":"https://openalex.org/W2597635960","doi":"https://doi.org/10.1109/vtcfall.2016.7880926","title":"CAF Diversity Combining for Spectrum Sensing by Test Statistics Sharing with Time-Averaged Weights","display_name":"CAF Diversity Combining for Spectrum Sensing by Test Statistics Sharing with Time-Averaged Weights","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2597635960","doi":"https://doi.org/10.1109/vtcfall.2016.7880926","mag":"2597635960"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2016.7880926","pdf_url":null,"source":{"id":"https://openalex.org/S4363607774","display_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5090485501","display_name":"Daiki Cho","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117807","display_name":"National Institute of Technology, Akashi College","ror":"https://ror.org/029kvcs29","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210117807","https://openalex.org/I4210120810"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Daiki Cho","raw_affiliation_strings":["Advanced Course of Mech. and Electron. Syst. Eng., Nat. Inst. of Technol., Akashi Coll., Akashi, Hyogo, Japan"],"affiliations":[{"raw_affiliation_string":"Advanced Course of Mech. and Electron. Syst. Eng., Nat. Inst. of Technol., Akashi Coll., Akashi, Hyogo, Japan","institution_ids":["https://openalex.org/I4210117807"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018681217","display_name":"Kondo Atsushi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117807","display_name":"National Institute of Technology, Akashi College","ror":"https://ror.org/029kvcs29","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210117807","https://openalex.org/I4210120810"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsushi Kondo","raw_affiliation_strings":["Dept. of Elect. and Comput. Eng., Nat. Inst. of Technol., Akashi Coll. Japan, Akashi, Hyogo, Japan"],"affiliations":[{"raw_affiliation_string":"Dept. of Elect. and Comput. Eng., Nat. Inst. of Technol., Akashi Coll. Japan, Akashi, Hyogo, Japan","institution_ids":["https://openalex.org/I4210117807"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029095117","display_name":"Shusuke Narieda","orcid":"https://orcid.org/0000-0003-2398-9977"},"institutions":[{"id":"https://openalex.org/I4210117807","display_name":"National Institute of Technology, Akashi College","ror":"https://ror.org/029kvcs29","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210117807","https://openalex.org/I4210120810"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shusuke Narieda","raw_affiliation_strings":["Dept. of Elect. and Comput. Eng., Nat. Inst. of Technol., Akashi Coll. Japan, Akashi, Hyogo, Japan"],"affiliations":[{"raw_affiliation_string":"Dept. of Elect. and Comput. Eng., Nat. Inst. of Technol., Akashi Coll. Japan, Akashi, Hyogo, Japan","institution_ids":["https://openalex.org/I4210117807"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076133663","display_name":"Kenta Umebayashi","orcid":"https://orcid.org/0000-0002-4669-7187"},"institutions":[{"id":"https://openalex.org/I92614990","display_name":"Tokyo University of Agriculture and Technology","ror":"https://ror.org/00qg0kr10","country_code":"JP","type":"funder","lineage":["https://openalex.org/I92614990"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kenta Umebayashi","raw_affiliation_strings":["Dept. of Elect. and Electron. Eng., Tokyo Univ. of Agric. and Technol., Koganei, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Dept. of Elect. and Electron. Eng., Tokyo Univ. of Agric. and Technol., Koganei, Tokyo, Japan","institution_ids":["https://openalex.org/I92614990"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.218,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":1,"citation_normalized_percentile":{"value":0.232225,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":66,"max":73},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":1.0,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9992,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9985,"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/diversity-combining","display_name":"Diversity combining","score":0.5402747},{"id":"https://openalex.org/keywords/diversity-scheme","display_name":"Diversity scheme","score":0.45251444},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.43476993}],"concepts":[{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.78054404},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.5521596},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.54786664},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5473178},{"id":"https://openalex.org/C136817519","wikidata":"https://www.wikidata.org/wiki/Q947493","display_name":"Diversity combining","level":4,"score":0.5402747},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5067399},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.5032777},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.47229388},{"id":"https://openalex.org/C198746922","wikidata":"https://www.wikidata.org/wiki/Q1230593","display_name":"Diversity scheme","level":4,"score":0.45251444},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4502534},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.43476993},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3500607},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.2095145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20301336},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11874327},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","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},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2016.7880926","pdf_url":null,"source":{"id":"https://openalex.org/S4363607774","display_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":11,"referenced_works":["https://openalex.org/W1549287367","https://openalex.org/W1916194272","https://openalex.org/W2033686181","https://openalex.org/W2071707134","https://openalex.org/W2073277820","https://openalex.org/W2084436032","https://openalex.org/W2115400030","https://openalex.org/W2149255982","https://openalex.org/W2155999145","https://openalex.org/W2156043963","https://openalex.org/W2183081997"],"related_works":["https://openalex.org/W2161513864","https://openalex.org/W2158377753","https://openalex.org/W2143399769","https://openalex.org/W2130684019","https://openalex.org/W2124277758","https://openalex.org/W2122821788","https://openalex.org/W2114298946","https://openalex.org/W2042097629","https://openalex.org/W2037489944","https://openalex.org/W1981390340"],"abstract_inverted_index":{"This":[0,47],"paper":[1],"presents":[2],"a":[3,28,39,59],"cyclic":[4],"autocorrelation":[5],"function":[6],"(CAF)":[7],"diversity":[8,31],"combining":[9,32],"technique":[10,48],"for":[11],"spectrum":[12,72],"sensing":[13],"using":[14],"test":[15],"statistics":[16],"shared":[17],"among":[18],"multiple":[19],"receive":[20],"antennas":[21],"with":[22,79,102],"time-averaged":[23],"weights.":[24],"We":[25],"recently":[26],"reported":[27],"weighted":[29],"CAF":[30],"technique,":[33],"however,":[34],"the":[35,45,50,69,87,96,103],"weight":[36,55],"factor":[37],"has":[38],"noise":[40,51],"component,":[41],"which":[42],"negatively":[43],"affects":[44],"performance.":[46],"reduces":[49],"component":[52],"included":[53],"in":[54,100],"factors,":[56],"by":[57],"averaging":[58],"large":[60],"number":[61],"of":[62,71],"CAFs,":[63],"and":[64,83],"we":[65],"attempt":[66],"to":[67],"improve":[68],"performance":[70,90],"sensing.":[73],"The":[74],"presented":[75],"results":[76],"are":[77],"compared":[78],"some":[80],"conventional":[81,104],"techniques,":[82],"they":[84],"show":[85],"that":[86],"signal":[88],"detection":[89],"can":[91],"be":[92],"significantly":[93],"improved":[94],"without":[95],"increasing":[97],"computational":[98],"complexities":[99],"comparison":[101],"techniques.":[105]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2597635960","counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2025-04-20T01:05:55.918355","created_date":"2017-04-07"}