{"id":"https://openalex.org/W4417281977","doi":"https://doi.org/10.1109/pimrc62392.2025.11274662","title":"Network KPI Prediction in Mobile Networks Using CNN-LSTM Time Series Model","display_name":"Network KPI Prediction in Mobile Networks Using CNN-LSTM Time Series Model","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4417281977","doi":"https://doi.org/10.1109/pimrc62392.2025.11274662"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc62392.2025.11274662","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc62392.2025.11274662","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vbn.aau.dk/ws/files/790088943/1571164685_paper.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002151989","display_name":"Poonam Maurya","orcid":"https://orcid.org/0000-0001-6357-1749"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Poonam Maurya","raw_affiliation_strings":["Aalborg University,Department of Electronic Systems,Denmark"],"affiliations":[{"raw_affiliation_string":"Aalborg University,Department of Electronic Systems,Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089307132","display_name":"Alejandro Ram\u00edrez-Arroyo","orcid":"https://orcid.org/0000-0001-8647-6103"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Alejandro Ram\u00edrez-Arroyo","raw_affiliation_strings":["Aalborg University,Department of Electronic Systems,Denmark"],"affiliations":[{"raw_affiliation_string":"Aalborg University,Department of Electronic Systems,Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010818634","display_name":"Troels B. S\u00f8rensen","orcid":"https://orcid.org/0000-0002-7940-7190"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Troels Bundgaard S\u00f8rensen","raw_affiliation_strings":["Aalborg University,Department of Electronic Systems,Denmark"],"affiliations":[{"raw_affiliation_string":"Aalborg University,Department of Electronic Systems,Denmark","institution_ids":["https://openalex.org/I891191580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002151989"],"corresponding_institution_ids":["https://openalex.org/I891191580"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3919821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.1160999983549118,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.1160999983549118,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.10729999840259552,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.07880000025033951,"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/performance-indicator","display_name":"Performance indicator","score":0.8384000062942505},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5253999829292297},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.49790000915527344},{"id":"https://openalex.org/keywords/network-performance","display_name":"Network performance","score":0.4885999858379364},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.4553999900817871},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/quality-of-service","display_name":"Quality of service","score":0.43050000071525574},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.40849998593330383},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.3887999951839447}],"concepts":[{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.8384000062942505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7166000008583069},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5253999829292297},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C203274722","wikidata":"https://www.wikidata.org/wiki/Q7001161","display_name":"Network performance","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4810999929904938},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.4553999900817871},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4438999891281128},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.40849998593330383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3928999900817871},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3555999994277954},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.30169999599456787},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C95491727","wikidata":"https://www.wikidata.org/wiki/Q992968","display_name":"Mobile telephony","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C106365562","wikidata":"https://www.wikidata.org/wiki/Q3078360","display_name":"Radio access network","level":4,"score":0.272599995136261},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.26429998874664307},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C81877898","wikidata":"https://www.wikidata.org/wiki/Q1965787","display_name":"Network monitoring","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.25110000371932983},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/pimrc62392.2025.11274662","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc62392.2025.11274662","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/daa0b556-2ee1-4135-953d-28f4c135b097","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/daa0b556-2ee1-4135-953d-28f4c135b097","pdf_url":"https://vbn.aau.dk/ws/files/790088943/1571164685_paper.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Maurya, P, Ram\u00edrez Arroyo, A & S\u00f8rensen, T B 2025, Network KPI Prediction in Mobile Networks Using CNN-LSTM Time Series Model. in 2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)., 11274662, IEEE (Institute of Electrical and Electronics Engineers), IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops, 2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Istanbul, Turkey, 01/09/2025. https://doi.org/10.1109/PIMRC62392.2025.11274662","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:zenodo.org:18018984","is_oa":true,"landing_page_url":"https://doi.org/10.1109/PIMRC62392.2025.11274662","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,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PIMRC, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Istanbul, Turkiye, 1-4 September 2025","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/daa0b556-2ee1-4135-953d-28f4c135b097","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/daa0b556-2ee1-4135-953d-28f4c135b097","pdf_url":"https://vbn.aau.dk/ws/files/790088943/1571164685_paper.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Maurya, P, Ram\u00edrez Arroyo, A & S\u00f8rensen, T B 2025, Network KPI Prediction in Mobile Networks Using CNN-LSTM Time Series Model. in 2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)., 11274662, IEEE (Institute of Electrical and Electronics Engineers), IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops, 2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Istanbul, Turkey, 01/09/2025. https://doi.org/10.1109/PIMRC62392.2025.11274662","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1664815686","display_name":null,"funder_award_id":"101060881","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G2766541535","display_name":null,"funder_award_id":"101060881","funder_id":"https://openalex.org/F4320334322","funder_display_name":"HORIZON EUROPE Framework Programme"},{"id":"https://openalex.org/G8318064016","display_name":null,"funder_award_id":"Horizon","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320334322","display_name":"HORIZON EUROPE Framework Programme","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417281977.pdf","grobid_xml":"https://content.openalex.org/works/W4417281977.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,114],"of":[2,18,40,46,88,115,216],"network":[3,71,116,153,161,173,231],"key":[4,44],"performance":[5,101],"indicators":[6],"(KPIs),":[7],"such":[8],"as":[9],"throughput":[10,194],"and":[11,21,32,172,182,188,195,220],"latency,":[12],"is":[13,37,77],"important":[14],"for":[15,133,167,218,222],"maintaining":[16],"Quality":[17],"Service":[19],"(QoS)":[20],"enabling":[22],"intelligent,":[23],"proactive":[24],"optimization":[25],"in":[26,80,102,199,233],"mobile":[27],"applications":[28],"to":[29,54,60,84,99,111,140,227,229],"support":[30],"reliable":[31],"ubiquitous":[33],"connectivity.":[34],"One":[35],"application":[36],"the":[38,47,65,85,113,120,145,149,160,165,178,206],"monitoring":[39],"livestock":[41],"transport,":[42],"a":[43,56,104,168,234],"focus":[45],"Horizon":[48],"Europe":[49],"project":[50],"COMMECT,":[51],"which":[52,129],"aims":[53],"develop":[55],"smart":[57],"multi-connectivity":[58],"solution":[59],"ensure":[61],"seamless":[62],"connectivity":[63],"throughout":[64],"transport":[66],"route.":[67],"Although":[68],"beneficial,":[69],"predicting":[70],"KPIs":[72,76,154],"based":[73,155,190],"on":[74,156,191],"radio":[75,171],"particularly":[78],"challenging":[79],"dynamic":[81],"environments":[82],"due":[83],"intricate":[86],"nature":[87],"their":[89],"relationship.":[90],"In":[91,175],"our":[92,176],"previous":[93],"study,":[94],"this":[95],"weak":[96],"relationship":[97],"led":[98],"limited":[100],"applying":[103],"supervised":[105,146],"Random":[106],"Forest":[107],"(RF)":[108],"model.":[109],"Aiming":[110],"improve":[112],"KPIs,":[117],"we":[118],"investigate":[119],"convolutional":[121,131],"neural":[122],"network-long":[123],"short-term":[124],"memory":[125],"(CNN-LSTM)":[126],"time-series":[127],"model,":[128,148,208],"combines":[130],"layers":[132,139],"spatial":[134],"feature":[135],"extraction":[136],"with":[137],"LSTM":[138],"learn":[141],"temporal":[142],"dependencies.":[143],"Unlike":[144],"RF":[147,183,207],"current":[150],"approach":[151],"predicts":[152],"past":[157],"information":[158],"about":[159],"KPI":[162],"itself,":[163],"bypassing":[164],"need":[166],"mapping":[169],"between":[170],"KPIs.":[174],"comparison,":[177],"CNN-LSTM":[179,203],"time":[180],"series":[181],"models":[184],"have":[185],"been":[186],"trained":[187],"tested":[189],"measured":[192],"5G":[193],"latency":[196,219],"data":[197],"collected":[198],"northern":[200],"Denmark.":[201],"The":[202],"model":[204],"outperforms":[205],"achieving":[209],"an":[210],"R<sup":[211],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[212],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[213],"score":[214],"improvement":[215],"26.17%":[217],"15.81%":[221],"throughput,":[223],"highlighting":[224],"its":[225],"ability":[226],"adapt":[228],"varying":[230],"conditions":[232],"realistic":[235],"setting.":[236]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-12-12T00:00:00"}