{"id":"https://openalex.org/W3163312460","doi":"https://doi.org/10.1109/wcncw49093.2021.9420005","title":"A Spectrum Prediction Method for Bursty Frequency Bands","display_name":"A Spectrum Prediction Method for Bursty Frequency Bands","publication_year":2021,"publication_date":"2021-03-29","ids":{"openalex":"https://openalex.org/W3163312460","doi":"https://doi.org/10.1109/wcncw49093.2021.9420005","mag":"3163312460"},"language":"en","primary_location":{"id":"doi:10.1109/wcncw49093.2021.9420005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcncw49093.2021.9420005","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","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/A5083993732","display_name":"Chao Yang","orcid":"https://orcid.org/0000-0002-9658-050X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chao Yang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory, Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory, Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083405667","display_name":"Tao Peng","orcid":"https://orcid.org/0000-0003-1701-6255"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Peng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory, Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory, Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057706928","display_name":"Peiliang Zuo","orcid":"https://orcid.org/0000-0002-5466-1627"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiliang Zuo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory, Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory, Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100408892","display_name":"Xinyue Wang","orcid":"https://orcid.org/0009-0005-3851-5768"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyue Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory, Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory, Key Laboratory of Universal Wireless Communication, Ministry of Education,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083993732"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.4584,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64255781,"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":null,"last_page":null},"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":0.9986000061035156,"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":0.9986000061035156,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9944999814033508,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/burstiness","display_name":"Burstiness","score":0.7467095851898193},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7043301463127136},{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.5741716623306274},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5493929386138916},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5448765754699707},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.519827663898468},{"id":"https://openalex.org/keywords/premise","display_name":"Premise","score":0.49442362785339355},{"id":"https://openalex.org/keywords/radio-spectrum","display_name":"Radio spectrum","score":0.48675966262817383},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.4650700092315674},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4193423390388489},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.41226115822792053},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3929634094238281},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3635467290878296},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3404451608657837},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2114657759666443},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.14557760953903198},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09185686707496643}],"concepts":[{"id":"https://openalex.org/C2781023610","wikidata":"https://www.wikidata.org/wiki/Q17006304","display_name":"Burstiness","level":3,"score":0.7467095851898193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7043301463127136},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.5741716623306274},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5493929386138916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5448765754699707},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.519827663898468},{"id":"https://openalex.org/C2778023277","wikidata":"https://www.wikidata.org/wiki/Q321703","display_name":"Premise","level":2,"score":0.49442362785339355},{"id":"https://openalex.org/C92545706","wikidata":"https://www.wikidata.org/wiki/Q902174","display_name":"Radio spectrum","level":2,"score":0.48675966262817383},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.4650700092315674},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4193423390388489},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.41226115822792053},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3929634094238281},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3635467290878296},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3404451608657837},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2114657759666443},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.14557760953903198},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09185686707496643},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcncw49093.2021.9420005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcncw49093.2021.9420005","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4000000059604645}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2018021746","https://openalex.org/W2073459066","https://openalex.org/W2131647894","https://openalex.org/W2153090463","https://openalex.org/W2155753432","https://openalex.org/W2201581102","https://openalex.org/W2248378417","https://openalex.org/W2541571801","https://openalex.org/W2755056899","https://openalex.org/W2897505673","https://openalex.org/W2908487597","https://openalex.org/W2922273628","https://openalex.org/W2963477884","https://openalex.org/W2976021085","https://openalex.org/W3022426791","https://openalex.org/W3036375754","https://openalex.org/W3039599825","https://openalex.org/W6668990524"],"related_works":["https://openalex.org/W2236567279","https://openalex.org/W2188389332","https://openalex.org/W4225341666","https://openalex.org/W3046087850","https://openalex.org/W1988869880","https://openalex.org/W185479762","https://openalex.org/W3161816731","https://openalex.org/W2224800885","https://openalex.org/W2392764151","https://openalex.org/W2153872761"],"abstract_inverted_index":{"Spectrum":[0],"prediction":[1,23,75,136,147,200],"is":[2,90,105],"an":[3],"important":[4],"technology":[5],"for":[6,27,92],"spectrum":[7,22],"cognition":[8],"which":[9],"constitutes":[10],"the":[11,20,52,66,74,100,109,117,121,131,135,139,161,174,182,193,199],"premise":[12],"of":[13,19,51,69,76,83,123,134,184,198],"cognitive":[14],"radio":[15],"technology.":[16],"However,":[17],"most":[18],"existing":[21],"methods":[24,36],"were":[25],"proposed":[26,162],"common":[28],"frequency":[29],"bands.":[30,78],"Based":[31],"on":[32],"this":[33,127],"philosophy,":[34],"these":[35],"generally":[37],"build":[38],"a":[39],"mapping":[40],"model":[41,54],"between":[42],"historical":[43],"statistical":[44],"information":[45],"and":[46,49,96,112],"future":[47],"state,":[48],"performance":[50,148,183],"single":[53],"was":[55],"verified":[56],"to":[57,61,107,137,152,180],"be":[58,156],"limited":[59],"due":[60],"that":[62,189],"it":[63],"cannot":[64],"match":[65],"high":[67],"burstiness":[68],"spectrum.":[70],"This":[71],"paper":[72,128],"considers":[73],"bursty":[77],"The":[79,186],"previous":[80],"collected":[81,175],"data":[82,177],"2.4GHz":[84],"Industrial,":[85],"Scientific,":[86],"Medical":[87],"(ISM)":[88],"bands":[89],"utilized":[91],"both":[93],"feature":[94],"analysis":[95],"method":[97,104,163],"research.":[98],"Specifically,":[99],"deep-reinforcement":[101],"learning":[102,168],"(DRL)":[103],"adopted":[106],"address":[108],"high-dimensional":[110],"state":[111],"action":[113],"spaces":[114],"caused":[115],"by":[116],"classification":[118,140],"process":[119],"using":[120,149],"values":[122],"several":[124],"features.":[125],"Besides,":[126],"also":[129],"employs":[130],"upper-limit":[132],"results":[133,187],"refine":[138],"process,":[141],"i.e.":[142],"those":[143],"groups":[144],"with":[145,173],"better":[146],"predictors":[150],"corresponding":[151],"other":[153],"classes":[154],"will":[155],"readjusted.":[157],"We":[158],"thus":[159],"name":[160],"as":[164],"classification-based":[165],"deep":[166],"reinforcement":[167],"(C-DRL).":[169],"Finally,":[170],"extensive":[171],"experiments":[172],"real":[176],"are":[178],"conducted":[179],"verify":[181],"C-DRL.":[185],"suggest":[188],"C-DRL":[190],"significantly":[191],"outperforms":[192],"state-of-the-art":[194],"algorithms":[195],"in":[196],"terms":[197],"performance.":[201]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
