{"id":"https://openalex.org/W7165661837","doi":"https://doi.org/10.48550/arxiv.2606.23110","title":"LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN","display_name":"LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165661837","doi":"https://doi.org/10.48550/arxiv.2606.23110"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23110","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23110","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.23110","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139146409","display_name":"Rui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101591026","display_name":"Linchao Zhang","orcid":"https://orcid.org/0000-0001-8547-9273"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Linchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139137542","display_name":"Qiang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139179178","display_name":"Kun Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.1446000039577484,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.1446000039577484,"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/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.0966000035405159,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.08529999852180481,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7253000140190125},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.490200012922287},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4887999892234802},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.46070000529289246},{"id":"https://openalex.org/keywords/telecommunications-link","display_name":"Telecommunications link","score":0.4050999879837036},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.3865000009536743},{"id":"https://openalex.org/keywords/hybrid-automatic-repeat-request","display_name":"Hybrid automatic repeat request","score":0.38589999079704285},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.382099986076355}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7253000140190125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6869000196456909},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.490200012922287},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4887999892234802},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C138660444","wikidata":"https://www.wikidata.org/wiki/Q5607897","display_name":"Telecommunications link","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.3865000009536743},{"id":"https://openalex.org/C118437007","wikidata":"https://www.wikidata.org/wiki/Q1017139","display_name":"Hybrid automatic repeat request","level":3,"score":0.38589999079704285},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.382099986076355},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3686999976634979},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.322299987077713},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C2776639698","wikidata":"https://www.wikidata.org/wiki/Q4927140","display_name":"Block Error Rate","level":3,"score":0.29409998655319214},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2824000120162964},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C157607044","wikidata":"https://www.wikidata.org/wiki/Q5165851","display_name":"Control channel","level":3,"score":0.25360000133514404},{"id":"https://openalex.org/C138916503","wikidata":"https://www.wikidata.org/wiki/Q2714551","display_name":"Link adaptation","level":4,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23110","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23110","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23110","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23110","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Outer-loop":[0],"link":[1],"adaptation":[2],"(OLLA)":[3],"is":[4,111],"widely":[5],"deployed":[6],"in":[7],"5G":[8,123],"NR":[9,124],"to":[10,60,103,141,151,172,178],"track":[11],"channel":[12,137,174],"variations,":[13],"yet":[14],"its":[15],"reliance":[16],"on":[17,55,120],"first-order,":[18],"single-bit":[19],"feedback":[20],"degrades":[21],"performance":[22],"significantly":[23],"under":[24,88,130,134,182],"high-mobility":[25],"and":[26,73,176],"fast-varying":[27],"channels.":[28],"This":[29],"paper":[30],"presents":[31],"LOLLA":[32],"(Learned":[33],"Outer-Loop":[34],"Link":[35],"Adaptation),":[36],"a":[37,49,89,121,156],"deep":[38],"reinforcement":[39],"learning":[40],"framework":[41,110],"that":[42,159],"replaces":[43],"the":[44,65,76,114,186,189],"conventional":[45,77],"OLLA":[46,78,146,162],"staircase":[47],"with":[48],"learned,":[50],"continuous":[51],"SINR":[52],"offset":[53,63],"conditioned":[54],"rich":[56],"PHY/MAC":[57],"telemetry":[58],"inaccessible":[59],"OLLA.":[61],"The":[62,109,168],"modulates":[64],"SINR-to-MCS":[66],"lookup":[67],"table,":[68],"preserving":[69],"3GPP-compliant":[70],"MCS":[71],"selection":[72],"provably":[74],"subsuming":[75],"update":[79],"rule.":[80],"A":[81],"Proximal":[82],"Policy":[83],"Optimization":[84],"(PPO)":[85],"policy":[86,170],"trained":[87],"Lagrangian":[90],"block":[91],"error":[92],"rate":[93],"(BLER)":[94],"constraint":[95],"automatically":[96],"enforces":[97],"tunable":[98],"reliability":[99,166],"targets":[100],"from":[101],"1%":[102],"15%":[104,140],"without":[105],"manual":[106],"penalty":[107],"calibration.":[108],"realized":[112],"as":[113],"first":[115],"closed-loop":[116],"AI-native":[117],"control":[118,128],"dApp":[119],"GPU-accelerated":[122],"stack,":[125],"achieving":[126],"end-to-end":[127],"latencies":[129],"500":[131],"microseconds.":[132],"Evaluations":[133],"3GPP":[135],"TDL":[136],"models":[138,175],"demonstrate":[139],"92%":[142],"throughput":[143],"gains":[144],"over":[145],"across":[147,163],"Doppler":[148],"frequencies":[149],"up":[150],"400":[152],"Hz,":[153],"while":[154],"attaining":[155],"Pareto":[157],"frontier":[158],"strictly":[160],"dominates":[161],"all":[164],"evaluated":[165],"targets.":[167],"learned":[169],"generalizes":[171],"unseen":[173],"scales":[177],"eight":[179],"concurrent":[180],"UEs":[181],"shared-resource":[183],"scheduling.":[184],"In":[185],"uplink":[187],"formulation,":[188],"gNB":[190],"directly":[191],"observes":[192],"decoding":[193],"outcomes,":[194],"enabling":[195],"simulation-to-deployment":[196],"parity.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}