{"id":"https://openalex.org/W2936720645","doi":"https://doi.org/10.1109/iccnc.2019.8685527","title":"Generative Adversarial Learning for Machine Learning empowered Self Organizing 5G Networks","display_name":"Generative Adversarial Learning for Machine Learning empowered Self Organizing 5G Networks","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2936720645","doi":"https://doi.org/10.1109/iccnc.2019.8685527","mag":"2936720645"},"language":"en","primary_location":{"id":"doi:10.1109/iccnc.2019.8685527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccnc.2019.8685527","pdf_url":null,"source":{"id":"https://openalex.org/S4306498526","display_name":"2019 International Conference on Computing, Networking and Communications (ICNC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Computing, Networking and Communications (ICNC)","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/A5101594217","display_name":"Ben Richard Hughes","orcid":"https://orcid.org/0000-0001-6314-4683"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ben Hughes","raw_affiliation_strings":["Booker T. Washington High School, Tulsa, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Booker T. Washington High School, Tulsa, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014010940","display_name":"Shruti Bothe","orcid":"https://orcid.org/0000-0001-7481-8946"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]},{"id":"https://openalex.org/I87208437","display_name":"University of Tulsa","ror":"https://ror.org/04wn28048","country_code":"US","type":"education","lineage":["https://openalex.org/I87208437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shruti Bothe","raw_affiliation_strings":["University of Oklahoma, Tulsa, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oklahoma, Tulsa, USA","institution_ids":["https://openalex.org/I87208437","https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045353802","display_name":"Hasan Farooq","orcid":"https://orcid.org/0000-0002-1208-1245"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]},{"id":"https://openalex.org/I87208437","display_name":"University of Tulsa","ror":"https://ror.org/04wn28048","country_code":"US","type":"education","lineage":["https://openalex.org/I87208437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hasan Farooq","raw_affiliation_strings":["University of Oklahoma, Tulsa, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oklahoma, Tulsa, USA","institution_ids":["https://openalex.org/I87208437","https://openalex.org/I8692664"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100331811","display_name":"Muhammad Ali Imran","orcid":"https://orcid.org/0000-0003-4743-9136"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]},{"id":"https://openalex.org/I87208437","display_name":"University of Tulsa","ror":"https://ror.org/04wn28048","country_code":"US","type":"education","lineage":["https://openalex.org/I87208437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Imran","raw_affiliation_strings":["University of Oklahoma, Tulsa, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oklahoma, Tulsa, USA","institution_ids":["https://openalex.org/I87208437","https://openalex.org/I8692664"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.8427,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.98328388,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"282","last_page":"286"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9973000288009644,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9973000288009644,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.989799976348877,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9872000217437744,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7810887098312378},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7437542080879211},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6624747514724731},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6444342136383057},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5718793869018555},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5259373784065247},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5070364475250244},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5011477470397949},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4957354962825775},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.45203107595443726},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4467763900756836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7810887098312378},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7437542080879211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6624747514724731},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6444342136383057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5718793869018555},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5259373784065247},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5070364475250244},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5011477470397949},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4957354962825775},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.45203107595443726},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4467763900756836},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccnc.2019.8685527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccnc.2019.8685527","pdf_url":null,"source":{"id":"https://openalex.org/S4306498526","display_name":"2019 International Conference on Computing, Networking and Communications (ICNC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Computing, Networking and Communications (ICNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.5199999809265137},{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2099471712","https://openalex.org/W2151854870","https://openalex.org/W2157963512","https://openalex.org/W2160160578","https://openalex.org/W2279114194","https://openalex.org/W2545234387","https://openalex.org/W2739824434","https://openalex.org/W2770404886","https://openalex.org/W2963079272","https://openalex.org/W2963341071","https://openalex.org/W2963684088","https://openalex.org/W3122408561","https://openalex.org/W4320013936","https://openalex.org/W6685352114"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2502115930","https://openalex.org/W2127970246","https://openalex.org/W1001352512","https://openalex.org/W4382618745","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W2748922771","https://openalex.org/W1987128138"],"abstract_inverted_index":{"In":[0,97],"the":[1,55,64,70,73,136,155,166,179,195,202,205,218,233,236],"wake":[2],"of":[3,5,39,49,66,72,104,161,169,198,204,235],"diversity":[4],"service":[6],"requirements":[7],"and":[8,69,171,201,229,239],"increasing":[9],"push":[10],"for":[11,32,54,88,92],"extreme":[12],"efficiency,":[13],"adaptability":[14],"propelled":[15],"by":[16],"machine":[17],"learning":[18],"(ML)":[19],"a.k.a":[20],"self":[21],"organizing":[22],"networks":[23,56],"(SON)":[24],"is":[25,81,127,146],"emerging":[26],"as":[27,43,185],"an":[28,128,186],"inevitable":[29],"design":[30],"feature":[31],"future":[33],"mobile":[34,213],"5G":[35],"networks.":[36,124],"The":[37],"implementation":[38],"SON":[40,74,91],"with":[41,60,189,223],"ML":[42,89],"a":[44,102,199,211,224],"foundation":[45],"requires":[46],"significant":[47],"amounts":[48,160],"real":[50,78,212],"labeled":[51,79],"sample":[52,67,107,137,180,227],"data":[53,68,80,108,133,145],"to":[57,130,134,174,178],"train":[58],"on,":[59],"high":[61],"correlation":[62],"between":[63],"amount":[65],"effectiveness":[71],"algorithm.":[75],"As":[76],"generally":[77],"scarce":[82],"therefore":[83],"it":[84],"can":[85,157,220],"become":[86],"bottleneck":[87],"empowered":[90],"unleashing":[93],"their":[94],"true":[95,237],"potential.":[96],"this":[98],"work,":[99],"we":[100],"propose":[101],"method":[103,126,156,183,219],"expanding":[105],"these":[106],"sets":[109],"using":[110],"Generative":[111],"Adversarial":[112],"Networks":[113],"(GANs),":[114],"which":[115],"are":[116],"based":[117],"on":[118],"two":[119],"interconnected":[120],"deep":[121],"artificial":[122],"neural":[123],"This":[125,182],"alternative":[129],"taking":[131,143],"more":[132,144],"expand":[135],"set,":[138],"preferred":[139],"in":[140,207],"cases":[141],"where":[142],"not":[147],"simple,":[148],"feasible,":[149],"or":[150],"efficient.":[151],"We":[152],"demonstrate":[153],"how":[154],"generate":[158],"large":[159],"realistic":[162],"synthetic":[163,243],"data,":[164],"utilizing":[165],"GAN's":[167],"ability":[168],"generation":[170],"discrimination,":[172],"able":[173],"be":[175,221],"easily":[176],"added":[177],"set.":[181],"is,":[184],"example,":[187],"implemented":[188],"Call":[190],"Data":[191],"Records":[192],"(CDRs)":[193],"containing":[194],"start":[196],"hour":[197],"call":[200],"duration":[203],"call,":[206],"minutes":[208],"taken":[209],"from":[210],"operator.":[214],"Results":[215],"show":[216],"that":[217],"used":[222],"relatively":[225],"small":[226],"set":[228],"little":[230],"information":[231],"about":[232],"statistics":[234],"CDRs":[238],"still":[240],"make":[241],"accurate":[242],"ones.":[244]},"counts_by_year":[{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
