{"id":"https://openalex.org/W4410227178","doi":"https://doi.org/10.1109/wcnc61545.2025.10978635","title":"Interference Prediction Using Gaussian Process Regression and Management Framework for Critical Services in Local 6G Networks","display_name":"Interference Prediction Using Gaussian Process Regression and Management Framework for Critical Services in Local 6G Networks","publication_year":2025,"publication_date":"2025-03-24","ids":{"openalex":"https://openalex.org/W4410227178","doi":"https://doi.org/10.1109/wcnc61545.2025.10978635"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc61545.2025.10978635","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc61545.2025.10978635","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Wireless Communications and Networking Conference (WCNC)","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/A5070071905","display_name":"Syed Luqman Shah","orcid":"https://orcid.org/0000-0001-5792-0842"},"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":true,"raw_author_name":"Syed Luqman Shah","raw_affiliation_strings":["University of Oulu,6G Flagship, Centre for Wireless Communications (CWC),Oulu,Finland,FI-90014"],"affiliations":[{"raw_affiliation_string":"University of Oulu,6G Flagship, Centre for Wireless Communications (CWC),Oulu,Finland,FI-90014","institution_ids":["https://openalex.org/I98381234"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011675848","display_name":"Nurul Huda Mahmood","orcid":"https://orcid.org/0000-0002-1478-2272"},"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":"Nurul Huda Mahmood","raw_affiliation_strings":["University of Oulu,6G Flagship, Centre for Wireless Communications (CWC),Oulu,Finland,FI-90014"],"affiliations":[{"raw_affiliation_string":"University of Oulu,6G Flagship, Centre for Wireless Communications (CWC),Oulu,Finland,FI-90014","institution_ids":["https://openalex.org/I98381234"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035872239","display_name":"Matti Latva\u2010aho","orcid":"https://orcid.org/0000-0002-6261-0969"},"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":"Matti Latva-aho","raw_affiliation_strings":["University of Oulu,6G Flagship, Centre for Wireless Communications (CWC),Oulu,Finland,FI-90014"],"affiliations":[{"raw_affiliation_string":"University of Oulu,6G Flagship, Centre for Wireless Communications (CWC),Oulu,Finland,FI-90014","institution_ids":["https://openalex.org/I98381234"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5070071905"],"corresponding_institution_ids":["https://openalex.org/I98381234"],"apc_list":null,"apc_paid":null,"fwci":0.788,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.72791629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9259999990463257,"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":0.9259999990463257,"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":0.6609960198402405},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6451519727706909},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.584705650806427},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5376236438751221},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.5168288350105286},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.48058414459228516},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4178972542285919},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4150850176811218},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33318471908569336},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3321109116077423},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3262448310852051},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25601065158843994},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.18756407499313354},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15691792964935303}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6609960198402405},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6451519727706909},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.584705650806427},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5376236438751221},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5168288350105286},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.48058414459228516},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4178972542285919},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4150850176811218},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33318471908569336},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3321109116077423},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3262448310852051},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25601065158843994},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.18756407499313354},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15691792964935303},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc61545.2025.10978635","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc61545.2025.10978635","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2625332994","https://openalex.org/W2890934178","https://openalex.org/W3096599635","https://openalex.org/W3131761718","https://openalex.org/W3157306683","https://openalex.org/W3161247968","https://openalex.org/W4226345517","https://openalex.org/W4300850679","https://openalex.org/W4385270097","https://openalex.org/W4389763759","https://openalex.org/W4390534264","https://openalex.org/W4393058480","https://openalex.org/W4400188486","https://openalex.org/W4402159793"],"related_works":["https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4293503520","https://openalex.org/W4300066510","https://openalex.org/W4406204722","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655"],"abstract_inverted_index":{"Interference":[0],"prediction":[1,55,164],"and":[2,14,34,60,170,183],"resource":[3,35,142],"allocation":[4,36,143],"are":[5,113],"critical":[6],"challenges":[7],"in":[8,37,135],"mission-critical":[9],"applications":[10],"where":[11],"stringent":[12],"latency":[13],"reliability":[15],"constraints":[16],"must":[17],"be":[18,103],"met.":[19],"This":[20],"paper":[21],"proposes":[22],"a":[23,49,77,86,126,140,166],"novel":[24],"Gaussian":[25,50,87],"process":[26,88],"regression":[27],"(GPR)-based":[28],"framework":[29],"for":[30],"predictive":[31],"interference":[32,44,58],"management":[33],"future":[38,57],"6G":[39],"networks.":[40],"Firstly,":[41],"the":[42,54,98,121,156,171,179,189],"received":[43],"power":[45],"is":[46,159],"modeled":[47],"as":[48,109],"process,":[51],"enabling":[52],"both":[53],"of":[56,64,80,155],"values":[59],"their":[61],"corresponding":[62],"estimation":[63],"uncertainty":[65],"bounds.":[66],"Differently":[67],"from":[68,76],"conventional":[69],"machine":[70],"learning":[71],"methods":[72],"that":[73,95,145],"extract":[74],"patterns":[75],"given":[78],"set":[79,100],"data":[81,99,111],"without":[82],"any":[83],"prior":[84,133],"belief,":[85],"assigns":[89],"probability":[90],"distributions":[91],"to":[92,130,150],"different":[93],"functions":[94],"possibly":[96],"represent":[97],"which":[101],"can":[102],"further":[104],"updates":[105],"using":[106],"Bayes'":[107],"rule":[108],"more":[110],"points":[112,129],"observed.":[114],"For":[115],"instance,":[116],"unlike":[117],"deep":[118],"neural":[119],"networks,":[120],"GPR":[122],"model":[123],"requires":[124],"only":[125],"few":[127],"sample":[128],"update":[131],"its":[132],"beliefs":[134],"real-time.":[136],"Furthermore,":[137],"we":[138],"propose":[139],"proactive":[141],"scheme":[144],"dynamically":[146],"adjusts":[147],"resources":[148],"according":[149],"predicted":[151],"interference.":[152],"The":[153,175],"performance":[154],"proposed":[157],"approach":[158],"evaluated":[160],"against":[161],"two":[162],"benchmarks":[163],"schemes,":[165],"moving":[167,180],"average-based":[168,181],"estimator":[169,182],"ideal":[172],"genie-aided":[173,190],"estimator.":[174],"GPR-based":[176],"method":[177],"outperforms":[178],"achieves":[184],"near-optimal":[185],"performance,":[186],"closely":[187],"matching":[188],"benchmark.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}