{"id":"https://openalex.org/W7225613279","doi":"https://doi.org/10.5281/zenodo.24078275","title":"Auditing Deep Reinforcement Learning for O-RAN Network Slicing: Worst-Group Safety under Traffic Bursts and Jamming","display_name":"Auditing Deep Reinforcement Learning for O-RAN Network Slicing: Worst-Group Safety under Traffic Bursts and Jamming","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225613279","doi":"https://doi.org/10.5281/zenodo.24078275"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24078275","doi":"https://doi.org/10.5281/zenodo.24078275","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24078275","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24078275","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161348669","display_name":"Devarjya Purkayastha","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Purkayastha, Devarjya","raw_affiliation_strings":["Manipal Academy of Higher Education","Manipal Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manipal Academy of Higher Education","institution_ids":["https://openalex.org/I164861460"]},{"raw_affiliation_string":"Manipal Institute of Technology","institution_ids":["https://openalex.org/I164861460"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5161348669"],"corresponding_institution_ids":["https://openalex.org/I164861460"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.8560000061988831,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.8560000061988831,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.01889999955892563,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.013399999588727951,"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/traffic-bursts","display_name":"traffic bursts","score":0.7360000014305115},{"id":"https://openalex.org/keywords/deep-q-network","display_name":"deep Q-network","score":0.6660000085830688},{"id":"https://openalex.org/keywords/bootstrap-confidence-intervals","display_name":"bootstrap confidence intervals","score":0.6240000128746033},{"id":"https://openalex.org/keywords/distribution-shift","display_name":"distribution shift","score":0.7200000286102295}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5685999989509583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5508000254631042},{"id":"https://openalex.org/C2779079576","wikidata":"https://www.wikidata.org/wiki/Q17092823","display_name":"Jamming","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.4828999936580658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39890000224113464},{"id":"https://openalex.org/C2777632111","wikidata":"https://www.wikidata.org/wiki/Q1937518","display_name":"Reservation","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.290800005197525},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2696000039577484},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2517000138759613},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24078275","doi":"https://doi.org/10.5281/zenodo.24078275","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24078275","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.24078276","doi":"https://doi.org/10.5281/zenodo.24078276","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24078276","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24078275","doi":"https://doi.org/10.5281/zenodo.24078275","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24078275","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W6929474510","https://openalex.org/W4389104731","https://openalex.org/W7127577294","https://openalex.org/W4399882266","https://openalex.org/W4297310171","https://openalex.org/W7071617608","https://openalex.org/W6922406953","https://openalex.org/W4415481066","https://openalex.org/W4403510263","https://openalex.org/W4404688072"],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1],"learning":[2],"(DRL)":[3],"can":[4,18],"adapt":[5],"O-RAN":[6,225],"slice":[7],"allocations":[8],"to":[9,133,189,196],"changing":[10],"traffic":[11,23,214],"and":[12,57,91,112,124,141,163,195,218],"interference,":[13],"but":[14,71],"episode-average":[15],"URLLC":[16,206],"cost":[17],"hide":[19],"failures":[20],"during":[21],"short":[22],"surges.":[24],"We":[25],"audit":[26,212],"the":[27,33,43,65,75,190,197],"maximum":[28],"traffic-group":[29],"mean":[30,44,66],"cost;":[31],"because":[32],"groups":[34],"partition":[35],"an":[36,200],"episode,":[37],"this":[38],"worst-group":[39,76,100],"constraint":[40],"already":[41],"implies":[42],"constraint.":[45],"In":[46],"a":[47,134,142,146],"single-cell":[48],"benchmark":[49],"with":[50],"burst":[51],"traffic,":[52],"selective":[53],"jamming,":[54],"impaired":[55],"KPMs,":[56],"105":[58],"discrete":[59],"actions,":[60],"57":[61],"static":[62,193],"actions":[63],"satisfy":[64,74],"limit":[67,77],"by":[68],"point":[69],"estimate":[70],"only":[72,204],"46":[73],"on":[78,102,150,161,182],"100":[79],"fresh":[80],"episodes;":[81],"interval":[82],"estimates":[83],"classify":[84,116],"45":[85],"as":[86,89,93,122,126,221],"supported,":[87],"10":[88],"uncertain,":[90],"50":[92],"violating.":[94],"The":[95,210],"validation-selected":[96],"mean-feasible":[97],"action":[98,194],"is":[99],"uncertain":[101],"those":[103],"traces.":[104],"Across":[105],"five":[106,178],"seeds":[107],"of":[108,118,158,224],"PPO-Lagrangian,":[109,111],"recurrent":[110,129],"DQN,":[113],"bootstrap":[114],"intervals":[115],"13":[117],"15":[119],"final":[120,130],"states":[121],"violating":[123],"two":[125],"uncertain;":[127],"every":[128],"policy":[131],"reduces":[132],"constant":[135],"catalogue":[136],"action.":[137],"A":[138,173],"privileged":[139],"oracle":[140],"delayed-load":[143],"heuristic,":[144],"both":[145,183],"two-action":[147],"rule":[148],"tuned":[149],"validation":[151],"episodes,":[152],"safely":[153],"recover":[154],"about":[155],"4.2":[156],"return":[157,188],"adaptive":[159],"headroom":[160],"training-matched":[162],"constant-jammer":[164],"traces,":[165],"which":[166],"no":[167],"evaluated":[168,180],"learner":[169],"or":[170],"shield":[171],"does.":[172],"calibrated":[174],"risk":[175],"ensemble":[176],"has":[177],"supported":[179],"runs":[181],"scenarios,":[184],"yet":[185],"it":[186],"loses":[187],"best":[191],"safe":[192],"heuristic.":[198],"Under":[199],"unseen":[201],"sweep":[202],"jammer,":[203],"50%":[205],"reservation":[207],"remains":[208],"supported.":[209],"resulting":[211],"establishes":[213],"group,":[215],"seed,":[216],"checkpoint,":[217],"distribution":[219],"shift":[220],"distinct":[222],"dimensions":[223],"DRL":[226],"safety":[227],"evaluation.":[228]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}