{"id":"https://openalex.org/W4416437119","doi":"https://doi.org/10.1109/icc59461.2026.11586950","title":"AoI-Aware Machine Learning for Constrained Multimodal Sensing-Aided Communications","display_name":"AoI-Aware Machine Learning for Constrained Multimodal Sensing-Aided Communications","publication_year":2026,"publication_date":"2026-05-24","ids":{"openalex":"https://openalex.org/W4416437119","doi":"https://doi.org/10.1109/icc59461.2026.11586950"},"language":null,"primary_location":{"id":"doi:10.1109/icc59461.2026.11586950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc59461.2026.11586950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2026 - IEEE International Conference on Communications","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.01406","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052438586","display_name":"Abolfazl Zakeri","orcid":"https://orcid.org/0000-0001-6577-1568"},"institutions":[{"id":"https://openalex.org/I98381234","display_name":"University of Oulu","ror":"https://ror.org/03yj89h83","country_code":"FI","type":"education","lineage":["https://openalex.org/I98381234"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Abolfazl Zakeri","raw_affiliation_strings":["University of Oulu,CWC-RT,Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oulu,CWC-RT,Finland","institution_ids":["https://openalex.org/I98381234"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026216458","display_name":"Nhan T. Nguyen","orcid":"https://orcid.org/0000-0001-6961-0147"},"institutions":[{"id":"https://openalex.org/I98381234","display_name":"University of Oulu","ror":"https://ror.org/03yj89h83","country_code":"FI","type":"education","lineage":["https://openalex.org/I98381234"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Nhan Thanh Nguyen","raw_affiliation_strings":["University of Oulu,CWC-RT,Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oulu,CWC-RT,Finland","institution_ids":["https://openalex.org/I98381234"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003243464","display_name":"Ahmed Alkhateeb","orcid":"https://orcid.org/0000-0001-5648-1569"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed Alkhateeb","raw_affiliation_strings":["Arizona State University,The School of Electrical, Computer, and Energy Engineering,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University,The School of Electrical, Computer, and Energy Engineering,USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029534229","display_name":"Markku Juntti","orcid":"https://orcid.org/0000-0002-5413-1896"},"institutions":[{"id":"https://openalex.org/I98381234","display_name":"University of Oulu","ror":"https://ror.org/03yj89h83","country_code":"FI","type":"education","lineage":["https://openalex.org/I98381234"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Markku Juntti","raw_affiliation_strings":["University of Oulu,CWC-RT,Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oulu,CWC-RT,Finland","institution_ids":["https://openalex.org/I98381234"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.9749000072479248,"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/T13553","display_name":"Age of Information Optimization","score":0.9749000072479248,"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/T12079","display_name":"IoT Networks and Protocols","score":0.00930000003427267,"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/T11458","display_name":"Advanced Wireless Communication Technologies","score":0.0017999999690800905,"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/inference","display_name":"Inference","score":0.5978999733924866},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5848000049591064},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5485000014305115},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5437999963760376},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5116999745368958},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3862000107765198},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.3384999930858612},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.33649998903274536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7480000257492065},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6402999758720398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6297000050544739},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5978999733924866},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5848000049591064},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5485000014305115},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5437999963760376},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5116999745368958},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3181000053882599},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.29910001158714294},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C94487597","wikidata":"https://www.wikidata.org/wiki/Q11101","display_name":"Sensory system","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C2986395286","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy inference","level":5,"score":0.2581999897956848},{"id":"https://openalex.org/C19889080","wikidata":"https://www.wikidata.org/wiki/Q2835852","display_name":"Beam search","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icc59461.2026.11586950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc59461.2026.11586950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2026 - IEEE International Conference on Communications","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2511.01406","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.01406","pdf_url":"https://arxiv.org/pdf/2511.01406","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.01406","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.01406","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":"pmh:oai:arXiv.org:2511.01406","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.01406","pdf_url":"https://arxiv.org/pdf/2511.01406","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4416437119.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Using":[0],"environmental":[1],"sensory":[2,22,68],"data":[3,23,69,115],"can":[4],"enhance":[5],"communications":[6],"beam":[7,51,90,104,134],"training":[8,127,160],"and":[9,50,132,163,169],"reduce":[10],"its":[11],"overhead":[12],"compared":[13],"to":[14,30,140],"conventional":[15],"methods.":[16],"However,":[17],"the":[18,60,95,99,102,106,110,118,126,130,133,147,183],"availability":[19],"of":[20,112,120,128],"fresh":[21,67,114],"during":[24,116],"inference":[25,165],"may":[26],"be":[27,71],"limited":[28,113],"due":[29],"sensing":[31,49,62,96,149,175,184],"constraints":[32],"or":[33],"sensor":[34],"failures,":[35],"necessitating":[36],"a":[37,46,57,81,86,142,154,173],"realistic":[38],"model":[39],"for":[40],"multimodal":[41,48],"sensing.":[42],"This":[43],"paper":[44],"proposes":[45],"joint":[47],"prediction":[52],"framework":[53],"that":[54,145,158],"operates":[55],"under":[56,172],"constraint":[58,185],"on":[59,153],"average":[61,148],"rate,":[63],"i.e.,":[64,80],"how":[65],"often":[66],"should":[70],"obtained.":[72],"The":[73,92,177],"proposed":[74],"method":[75],"combines":[76],"deep":[77,82],"reinforcement":[78],"learning,":[79],"Q-network":[83],"(DQN),":[84],"with":[85],"neural":[87],"network":[88],"(NN)-based":[89],"predictor.":[91,135],"DQN":[93,131],"determines":[94],"decisions,":[97],"while":[98],"NN":[100],"predicts":[101],"best":[103],"from":[105],"codebook.":[107],"To":[108],"capture":[109],"effect":[111],"inference,":[117],"age":[119],"information":[121],"(AoI)":[122],"is":[123,138,186],"incorporated":[124],"into":[125],"both":[129],"Lyapunov":[136],"optimization":[137],"employed":[139],"design":[141],"reward":[143],"function":[144],"enforces":[146],"constraint.":[150,176],"Simulation":[151],"results":[152],"real-world":[155],"dataset":[156],"show":[157],"AoI-aware":[159],"improves":[161],"top-1":[162],"top-3":[164],"accuracy":[166],"by":[167],"44.16%":[168],"52.96%,":[170],"respectively,":[171],"strict":[174],"performance":[178],"gain,":[179],"however,":[180],"diminishes":[181],"as":[182],"relaxed.":[187]},"counts_by_year":[],"updated_date":"2026-07-16T05:54:29.318815","created_date":"2025-11-06T00:00:00"}
