{"id":"https://openalex.org/W4409156588","doi":"https://doi.org/10.1109/ieeeconf60004.2024.10942833","title":"Robust Antenna Parameter Optimization in Cellular Networks via Adversarial Machine Learning","display_name":"Robust Antenna Parameter Optimization in Cellular Networks via Adversarial Machine Learning","publication_year":2024,"publication_date":"2024-10-27","ids":{"openalex":"https://openalex.org/W4409156588","doi":"https://doi.org/10.1109/ieeeconf60004.2024.10942833"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf60004.2024.10942833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf60004.2024.10942833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 58th Asilomar Conference on Signals, Systems, and Computers","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/A5010482697","display_name":"Ezgi Tekgul","orcid":"https://orcid.org/0000-0001-7724-0000"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ezgi Tekgul","raw_affiliation_strings":["The University of Texas,6G@UT,Dept. of ECE,Austin,USA"],"affiliations":[{"raw_affiliation_string":"The University of Texas,6G@UT,Dept. of ECE,Austin,USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022269967","display_name":"Salam Akoum","orcid":null},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Salam Akoum","raw_affiliation_strings":["AT&T Labs,Austin,USA"],"affiliations":[{"raw_affiliation_string":"AT&T Labs,Austin,USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042833857","display_name":"Jeffrey G. Andrews","orcid":"https://orcid.org/0000-0002-9115-5088"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeffrey G. Andrews","raw_affiliation_strings":["The University of Texas,6G@UT,Dept. of ECE,Austin,USA"],"affiliations":[{"raw_affiliation_string":"The University of Texas,6G@UT,Dept. of ECE,Austin,USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010482697"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16908409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1178","last_page":"1182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11946","display_name":"Antenna Design and Optimization","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11946","display_name":"Antenna Design and Optimization","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10069","display_name":"Antenna Design and Analysis","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6809629201889038},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.624955415725708},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4783620238304138},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34647026658058167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6809629201889038},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.624955415725708},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4783620238304138},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34647026658058167}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf60004.2024.10942833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf60004.2024.10942833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 58th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2010630450","https://openalex.org/W2082228576","https://openalex.org/W2156194072","https://openalex.org/W2620765670","https://openalex.org/W2900038292","https://openalex.org/W3047492485","https://openalex.org/W3212981375","https://openalex.org/W4206211608","https://openalex.org/W4285193396","https://openalex.org/W4310935318","https://openalex.org/W4360619614","https://openalex.org/W4386824759","https://openalex.org/W6675823452"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Cell":[0],"shaping":[1,33],"optimizes":[2],"coverage":[3,12],"and":[4,43,75,85,101,141],"capacity":[5],"in":[6,50],"cellular":[7],"networks":[8],"by":[9,69,131],"aligning":[10],"cell":[11,32],"areas":[13],"with":[14],"the":[15,29,48,51,120,125,146,156],"user":[16,41,52,107],"distribution":[17,24,53],"through":[18],"antenna":[19,82,151],"parameter":[20,112],"tuning.":[21],"As":[22],"this":[23],"changes":[25],"over":[26],"time,":[27],"ensuring":[28,111],"robustness":[30,113],"of":[31,119],"involves":[34],"(i)":[35],"maintaining":[36],"consistent":[37],"performance":[38,94],"across":[39],"various":[40],"distributions":[42,108],"(ii)":[44],"triggering":[45],"retraining":[46],"when":[47],"change":[49],"is":[54,122],"significant":[55],"enough":[56],"to":[57,150],"erode":[58],"performance.":[59],"This":[60],"work":[61],"proposes":[62],"a":[63,77,135],"framework":[64,90,121],"that":[65],"achieves":[66],"these":[67],"objectives":[68],"leveraging":[70],"adversarial":[71,147],"machine":[72],"learning":[73],"techniques":[74],"introducing":[76],"dissimilarity":[78],"metric":[79],"based":[80,153],"on":[81,155],"gain":[83,140,144],"patterns":[84],"path":[86],"loss.":[87],"Our":[88],"proposed":[89],"maximizes":[91],"network":[92,128],"key":[93],"metrics":[95],"-":[96,104],"such":[97],"as":[98],"data":[99],"rate":[100],"outage":[102],"probability":[103],"for":[105,145],"given":[106,157],"while":[109],"also":[110],"against":[114],"worst-case":[115],"scenarios.":[116],"The":[117],"effectiveness":[118],"demonstrated":[123],"using":[124],"state-of-the-art":[126],"5G":[127],"simulator":[129],"developed":[130],"AT&T":[132],"Labs,":[133],"revealing":[134],"4.3":[136],"dB":[137],"mean":[138],"SINR":[139],"22.7%":[142],"sum-log-rate":[143],"layout":[148],"compared":[149],"tuning":[152],"solely":[154],"layout.":[158]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}