{"id":"https://openalex.org/W2968392233","doi":"https://doi.org/10.1145/3341216.3342217","title":"Hierarchical Bayesian Modelling for Wireless Cellular Networks","display_name":"Hierarchical Bayesian Modelling for Wireless Cellular Networks","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2968392233","doi":"https://doi.org/10.1145/3341216.3342217","mag":"2968392233"},"language":"en","primary_location":{"id":"doi:10.1145/3341216.3342217","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341216.3342217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 Workshop on Network Meets AI & ML - NetAI'19","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/A5005336514","display_name":"Deniz \u00dcstebay","orcid":null},"institutions":[{"id":"https://openalex.org/I4210115038","display_name":"Huawei Technologies (Canada)","ror":"https://ror.org/026venb53","country_code":"CA","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210115038"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Deniz \u00dcstebay","raw_affiliation_strings":["Huawei Noah's Ark Lab, Montr\u00e9al, Qu\u00e9bec, Canada"],"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Montr\u00e9al, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I4210115038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089698321","display_name":"Jie Chuai","orcid":"https://orcid.org/0000-0001-9171-0697"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Chuai","raw_affiliation_strings":["Huawei Noah's Ark Lab, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Hong Kong, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5005336514"],"corresponding_institution_ids":["https://openalex.org/I4210115038"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08963514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"76","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9987999796867371,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9987999796867371,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.7910135984420776},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7567348480224609},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.546403706073761},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5234115719795227},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.5218638181686401},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5151387453079224},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4979822635650635},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.461970716714859},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4596436023712158},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.42288467288017273},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3713921904563904},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.356511652469635},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3400116562843323},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.17098170518875122},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09789067506790161},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08920648694038391},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07895007729530334}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7910135984420776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7567348480224609},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.546403706073761},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5234115719795227},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.5218638181686401},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5151387453079224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4979822635650635},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.461970716714859},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4596436023712158},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.42288467288017273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3713921904563904},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.356511652469635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3400116562843323},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.17098170518875122},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09789067506790161},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08920648694038391},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07895007729530334},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3341216.3342217","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341216.3342217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 Workshop on Network Meets AI & ML - NetAI'19","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1492039246","https://openalex.org/W1748357756","https://openalex.org/W1781644248","https://openalex.org/W1981457167","https://openalex.org/W2045656233","https://openalex.org/W2052534833","https://openalex.org/W2103720633","https://openalex.org/W2110144231","https://openalex.org/W2143104527","https://openalex.org/W2157801062","https://openalex.org/W2163386439","https://openalex.org/W2621303274","https://openalex.org/W2731461838","https://openalex.org/W2784007778","https://openalex.org/W2786244127","https://openalex.org/W2800895662","https://openalex.org/W2919034928","https://openalex.org/W2963977107","https://openalex.org/W3104312965","https://openalex.org/W4248681815"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W3103989898","https://openalex.org/W4287804464","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"With":[0],"the":[1,53,56,70,83,90,97,123,175],"recent":[2],"advances":[3],"in":[4,66,100],"wireless":[5,38],"technologies,":[6],"base":[7],"stations":[8],"are":[9,15],"becoming":[10],"more":[11,20],"sophisticated.":[12],"Network":[13],"operators":[14,168],"also":[16],"able":[17],"to":[18,22,51,62,118],"collect":[19],"data":[21,124],"improve":[23],"network":[24,68,167,170],"performance":[25,36,176],"and":[26,69,95,115,137,184],"user":[27],"experience.":[28],"In":[29],"this":[30,146,149,178],"paper":[31],"we":[32],"concentrate":[33],"on":[34,180],"modeling":[35,43,106],"of":[37,58,72,122,177],"cells":[39,65,86,101],"using":[40],"hierarchical":[41],"Bayesian":[42,105],"framework.":[44],"This":[45,128],"framework":[46],"provides":[47,155],"a":[48,67,108,133,143,181,185,190],"principled":[49],"way":[50],"navigate":[52],"space":[54],"between":[55,85,110],"option":[57,71,81],"creating":[59,73],"one":[60],"model":[61,150],"represent":[63],"all":[64,126],"separate":[74],"models":[75],"at":[76],"each":[77,140,159],"cell.":[78],"The":[79],"former":[80],"ignores":[82,96],"variations":[84],"(complete":[87],"pooling)":[88],"whereas":[89],"latter":[91],"is":[92,129,142,151],"overly":[93],"noisy":[94],"common":[98],"patterns":[99],"(no":[102],"pooling).":[103],"Hierarchical":[104],"strikes":[107],"trade-off":[109],"these":[111],"two":[112],"extreme":[113],"cases":[114],"enables":[116],"us":[117],"do":[119],"partial":[120],"pooling":[121],"from":[125,145,189],"cells.":[127],"done":[130],"by":[131,166],"estimating":[132],"parametric":[134],"population":[135],"distribution":[136],"assuming":[138],"that":[139],"cell":[141],"sample":[144],"distribution.":[147],"Because":[148],"fully":[152],"Bayesian,":[153],"it":[154],"uncertainty":[156],"intervals":[157],"around":[158],"estimated":[160],"parameter":[161],"which":[162],"can":[163],"be":[164],"used":[165],"making":[169],"management":[171],"decisions.":[172],"We":[173],"examine":[174],"method":[179],"synthetic":[182],"dataset":[183,187],"real":[186],"collected":[188],"cellular":[191],"network.":[192]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}