{"id":"https://openalex.org/W1987615181","doi":"https://doi.org/10.1109/infcom.2013.6566941","title":"Channel quality prediction based on Bayesian inference in cognitive radio networks","display_name":"Channel quality prediction based on Bayesian inference in cognitive radio networks","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W1987615181","doi":"https://doi.org/10.1109/infcom.2013.6566941","mag":"1987615181"},"language":"en","primary_location":{"id":"doi:10.1109/infcom.2013.6566941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infcom.2013.6566941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 Proceedings IEEE INFOCOM","raw_type":"proceedings-article"},"type":"conference-paper","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/A5087934370","display_name":"Xiaoshuang Xing","orcid":"https://orcid.org/0000-0003-0381-1389"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoshuang Xing","raw_affiliation_strings":["School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China","Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, , China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080987681","display_name":"Tao Jing","orcid":"https://orcid.org/0000-0001-5277-1729"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Jing","raw_affiliation_strings":["School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China","Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, , China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049709192","display_name":"Yan Huo","orcid":"https://orcid.org/0000-0003-0647-1009"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Huo","raw_affiliation_strings":["School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China","Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, , China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100698224","display_name":"Hongjuan Li","orcid":"https://orcid.org/0000-0001-5384-9903"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongjuan Li","raw_affiliation_strings":["Department of Computer Science, George Washington University, Washington, DC, DC, USA","Department of Computer Science , George Washington University , Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Washington University, Washington, DC, DC, USA","institution_ids":["https://openalex.org/I193531525"]},{"raw_affiliation_string":"Department of Computer Science , George Washington University , Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100692488","display_name":"Xiuzhen Cheng","orcid":"https://orcid.org/0000-0001-5912-4647"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiuzhen Cheng","raw_affiliation_strings":["Department of Computer Science, George Washington University, Washington, DC, DC, USA","Department of Computer Science , George Washington University , Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Washington University, Washington, DC, DC, USA","institution_ids":["https://openalex.org/I193531525"]},{"raw_affiliation_string":"Department of Computer Science , George Washington University , Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":113,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1465","last_page":"1473"},"is_retracted":false,"is_paratext":false,"is_xpac":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":0.9987999796867371,"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":0.9951000213623047,"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/cognitive-radio","display_name":"Cognitive radio","score":0.8780315518379211},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6768700480461121},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6657198667526245},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5257599949836731},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4693357050418854},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.46759432554244995},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.4363382160663605},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.42581695318222046},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4250052571296692},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4049019515514374},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3817756175994873},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3145979046821594},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2674277722835541},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1883104145526886},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.15918827056884766},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15533295273780823},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.12016651034355164}],"concepts":[{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.8780315518379211},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6768700480461121},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6657198667526245},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5257599949836731},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4693357050418854},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.46759432554244995},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.4363382160663605},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.42581695318222046},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4250052571296692},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4049019515514374},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3817756175994873},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3145979046821594},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2674277722835541},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1883104145526886},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.15918827056884766},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15533295273780823},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.12016651034355164},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/infcom.2013.6566941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infcom.2013.6566941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 Proceedings IEEE INFOCOM","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.727.2411","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.727.2411","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.seas.gwu.edu/%7Echeng/Publications/2013/ChannelQualityPrediction-Infocom13.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1582002023","https://openalex.org/W1976285638","https://openalex.org/W1976407947","https://openalex.org/W1982575274","https://openalex.org/W1986202561","https://openalex.org/W1990781704","https://openalex.org/W2049082138","https://openalex.org/W2072990861","https://openalex.org/W2082219742","https://openalex.org/W2106186328","https://openalex.org/W2114371821","https://openalex.org/W2115603171","https://openalex.org/W2117525829","https://openalex.org/W2126643042","https://openalex.org/W2131257134","https://openalex.org/W2134254435","https://openalex.org/W2134834634","https://openalex.org/W2135725960","https://openalex.org/W2143535483","https://openalex.org/W2146843214","https://openalex.org/W2148588212","https://openalex.org/W2158604761","https://openalex.org/W2162333578","https://openalex.org/W2164402548","https://openalex.org/W2164701487","https://openalex.org/W2167466178","https://openalex.org/W2168180948","https://openalex.org/W2169089686","https://openalex.org/W2169223481","https://openalex.org/W2274250074","https://openalex.org/W2765786427","https://openalex.org/W6644233408","https://openalex.org/W6661638826","https://openalex.org/W6694308079"],"related_works":["https://openalex.org/W2181410425","https://openalex.org/W2051888740","https://openalex.org/W2382279859","https://openalex.org/W2046691252","https://openalex.org/W3203708548","https://openalex.org/W2384472869","https://openalex.org/W2132580384","https://openalex.org/W1973050875","https://openalex.org/W2003835194","https://openalex.org/W1996209778"],"abstract_inverted_index":{"The":[0,130],"problem":[1],"of":[2,42,67,82,127,149,157],"channel":[3,35,69,103,110,134,140,151],"quality":[4,104,141,164],"prediction":[5,142],"in":[6,12,51],"cognitive":[7],"radio":[8],"networks":[9],"is":[10,20,39],"investigated":[11],"this":[13],"paper.":[14],"First,":[15],"the":[16,31,34,43,46,52,56,61,64,68,72,83,94,108,125,138,145,150,153,158],"spectrum":[17,73,114,154,174,179],"sensing":[18,74,115,155],"process":[19],"modeled":[21],"as":[22],"a":[23,40,102],"Non-Stationary":[24],"Hidden":[25],"Markov":[26],"Model":[27],"(NSHMM),":[28],"which":[29,59],"captures":[30,144],"fact":[32],"that":[33,133],"state":[36,147],"transition":[37],"probability":[38],"function":[41],"time":[44],"interval":[45],"primary":[47],"user":[48],"has":[49,120],"stayed":[50],"current":[53],"state.":[54],"Then":[55],"model":[57],"parameters,":[58],"carry":[60],"information":[62],"about":[63],"expected":[65],"duration":[66,112,148],"states":[70],"and":[71,78,113,152,160,167],"accuracy":[75,77,156],"(detection":[76],"false":[79],"alarm":[80],"probability)":[81],"SU,":[84],"are":[85,98],"estimated":[86,95],"via":[87],"Bayesian":[88],"inference":[89],"with":[90],"Gibbs":[91],"sampling.":[92],"Finally,":[93],"NSHMM":[96],"parameters":[97],"employed":[99],"to":[100,107,123],"design":[101],"metric":[105],"according":[106],"predicted":[109],"idle":[111,146],"accuracy.":[116],"Extensive":[117],"simulation":[118],"study":[119],"been":[121],"performed":[122],"investigate":[124],"effectiveness":[126],"our":[128],"design.":[129],"results":[131],"indicate":[132],"ranking":[135],"based":[136],"on":[137],"proposed":[139],"mechanism":[143],"SUs,":[159],"provides":[161],"more":[162],"high":[163],"transmission":[165,170],"opportunities":[166],"higher":[168],"successful":[169],"rates":[171],"at":[172],"shorter":[173],"waiting":[175],"times":[176],"for":[177],"dynamic":[178],"access.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":17},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":12},{"year":2013,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
