{"id":"https://openalex.org/W7167045336","doi":"https://doi.org/10.48550/arxiv.2607.00324","title":"Queue-Aware Graph Reinforcement Learning for UAV-ISAC-Assisted Maritime Data Collection","display_name":"Queue-Aware Graph Reinforcement Learning for UAV-ISAC-Assisted Maritime Data Collection","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7167045336","doi":"https://doi.org/10.48550/arxiv.2607.00324"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.00324","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00324","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.00324","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139887629","display_name":"Bohan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Bohan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061281954","display_name":"M H Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139907919","display_name":"Haochen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Haochen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139921427","display_name":"Yongkang Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Yongkang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139926687","display_name":"Ning Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Ning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102214857","display_name":"Jie Nie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nie, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139925459","display_name":"Pei Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Pei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100692488","display_name":"Xiuzhen Cheng","orcid":"https://orcid.org/0000-0001-5912-4647"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Xiuzhen","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/T11133","display_name":"UAV Applications and Optimization","score":0.4708999991416931,"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/T11133","display_name":"UAV Applications and Optimization","score":0.4708999991416931,"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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.4052000045776367,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10249","display_name":"Distributed Control Multi-Agent Systems","score":0.04899999871850014,"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":0.6797999739646912},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.6624000072479248},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5964000225067139},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.5515000224113464},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5507000088691711},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5133000016212463},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4740999937057495},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.44029998779296875}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6797999739646912},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.6624000072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6312999725341797},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5964000225067139},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.5515000224113464},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5507000088691711},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5133000016212463},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.44029998779296875},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3855000138282776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3828999996185303},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.35429999232292175},{"id":"https://openalex.org/C2779847632","wikidata":"https://www.wikidata.org/wiki/Q30026","display_name":"Buoy","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33799999952316284},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32499998807907104},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.305400013923645},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2953000068664551},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28859999775886536},{"id":"https://openalex.org/C2984634286","wikidata":"https://www.wikidata.org/wiki/Q1331926","display_name":"Decision process","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.2696000039577484},{"id":"https://openalex.org/C192126672","wikidata":"https://www.wikidata.org/wiki/Q1068715","display_name":"Telecommunications network","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.00324","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00324","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.00324","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00324","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"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":[{"score":0.5760285258293152,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"high-altitude":[3],"platform":[4],"(HAP)-assisted":[5],"sparse":[6,41],"cooperative":[7],"integrated":[8],"sensing":[9],"and":[10,29,40,58,67,113,143,157,169,212,216],"communication":[11],"(ISAC)":[12],"for":[13,48],"UAV-enabled":[14],"ocean":[15],"monitoring.":[16],"A":[17,136,160],"fleet":[18],"of":[19,93],"rotary-wing":[20],"UAVs":[21],"senses":[22],"drifting":[23],"buoys,":[24],"collects":[25],"their":[26],"monitoring":[27],"data,":[28],"reports":[30],"local":[31],"posterior":[32,63],"estimates":[33],"to":[34,176,218],"a":[35,49,77,91,103,126,131,144,170,206],"HAP":[36],"that":[37,187],"performs":[38],"fusion":[39],"cooperation":[42],"control.":[43],"The":[44,71,96],"model":[45],"explicitly":[46],"accounts":[47],"spatially":[50],"correlated":[51],"sea-patch":[52],"field,":[53],"patch-aware":[54],"buoy":[55,89],"dynamics,":[56],"RCS-":[57],"clutter-aware":[59],"echo":[60],"sensing,":[61,108],"fused":[62],"Cram\u00e9r-Rao":[64],"bounds":[65],"(PCRBs),":[66],"propulsion-energy-limited":[68],"UAV":[69],"mobility.":[70],"long-horizon":[72,98],"objective":[73],"is":[74,100],"cast":[75],"as":[76,102],"queue-weighted":[78,194],"buffered-collection":[79],"Markov":[80],"decision":[81],"process":[82],"rather":[83],"than":[84],"instantaneous":[85],"throughput,":[86],"where":[87],"each":[88],"maintains":[90,205],"backlog":[92],"buffered":[94],"observations.":[95],"resulting":[97],"design":[99],"formulated":[101],"mixed":[104],"discrete-continuous":[105],"problem":[106],"with":[107],"communication,":[109],"mobility,":[110],"safety,":[111],"buffered-collection,":[112],"onboard-energy":[114],"constraints.":[115,159],"To":[116],"address":[117],"the":[118,188,192,201],"combinatorial":[119],"association":[120],"component":[121],"without":[122,221],"replacing":[123],"learning":[124],"by":[125,197],"deterministic":[127,203],"optimizer,":[128],"we":[129],"propose":[130],"structured":[132],"feasible-association":[133],"graph-MARL":[134],"framework.":[135],"heterogeneous":[137],"graph":[138],"encoder":[139],"produces":[140],"candidate-edge":[141],"logits,":[142],"masked":[145],"sequential":[146],"b-matching":[147],"policy":[148,190],"samples":[149],"legal":[150],"UAV-buoy":[151],"associations":[152],"while":[153],"exactly":[154],"satisfying":[155],"UAV-load":[156],"buoy-cluster":[158],"MAPPO-style":[161],"training":[162],"procedure,":[163],"an":[164],"independent":[165],"queue-state":[166],"value":[167],"critic,":[168],"consistency-verification":[171],"protocol":[172],"are":[173],"then":[174],"specified":[175],"support":[177],"reproducible":[178],"training.":[179],"Simulation":[180],"results":[181],"on":[182],"congested":[183],"maritime":[184],"scenarios":[185],"show":[186],"proposed":[189],"improves":[191],"cumulative":[193],"collection":[195],"utility":[196],"about":[198],"106\\%":[199],"over":[200],"rate-driven":[202],"decoder,":[204],"large":[207],"margin":[208],"across":[209],"sea-state":[210],"sweeps":[211],"medium-to-heavy":[213],"traffic":[214],"loads,":[215],"transfers":[217],"larger":[219],"networks":[220],"fine-tuning.":[222]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-03T00:00:00"}
