{"id":"https://openalex.org/W4389747923","doi":"https://doi.org/10.1109/jiot.2023.3342984","title":"AI-Enhanced Generalizable Scheme for Path Loss Prediction in LoRaWAN","display_name":"AI-Enhanced Generalizable Scheme for Path Loss Prediction in LoRaWAN","publication_year":2023,"publication_date":"2023-12-14","ids":{"openalex":"https://openalex.org/W4389747923","doi":"https://doi.org/10.1109/jiot.2023.3342984"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2023.3342984","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2023.3342984","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-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/A5101645581","display_name":"Mingyu Chen","orcid":"https://orcid.org/0009-0009-1873-0873"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingyu Chen","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-1873-0873","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100732087","display_name":"Yan Zhang","orcid":"https://orcid.org/0000-0002-2168-9674"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhang","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2168-9674","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021597140","display_name":"Zijie Ji","orcid":"https://orcid.org/0000-0001-5213-3380"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijie Ji","raw_affiliation_strings":["Institute of Telecommunication and Navigation Satellite, China Academy of Space Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5213-3380","affiliations":[{"raw_affiliation_string":"Institute of Telecommunication and Navigation Satellite, China Academy of Space Technology, Beijing, China","institution_ids":["https://openalex.org/I194716290"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011241163","display_name":"C\u00e9sar Briso-Rodr\u00edguez","orcid":"https://orcid.org/0000-0001-8219-9110"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"C\u00e9sar Briso-Rodr\u00edguez","raw_affiliation_strings":["Department of Signal Theory and Communications, Universidad Politécnica de Madrid, Madrid, Spain"],"raw_orcid":"https://orcid.org/0000-0001-8219-9110","affiliations":[{"raw_affiliation_string":"Department of Signal Theory and Communications, Universidad Politécnica de Madrid, Madrid, Spain","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040774752","display_name":"Kaien Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaien Zhang","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101645581"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.7665,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.72153864,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"11","issue":"8","first_page":"14593","last_page":"14606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12079","display_name":"IoT Networks and Protocols","score":0.9979000091552734,"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/T12079","display_name":"IoT Networks and Protocols","score":0.9979000091552734,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9977999925613403,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.989300012588501,"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":0.8183550238609314},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5461777448654175},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.533539891242981},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5219306945800781},{"id":"https://openalex.org/keywords/path-loss","display_name":"Path loss","score":0.4912952780723572},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4658305048942566},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46305012702941895},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.43107378482818604},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42627424001693726},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4259136915206909},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.425568163394928},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.34232479333877563},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2840641140937805},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.11606931686401367},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11245569586753845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8183550238609314},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5461777448654175},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.533539891242981},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5219306945800781},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.4912952780723572},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4658305048942566},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46305012702941895},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.43107378482818604},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42627424001693726},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4259136915206909},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.425568163394928},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.34232479333877563},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2840641140937805},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.11606931686401367},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11245569586753845},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2023.3342984","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2023.3342984","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.4000000059604645}],"awards":[{"id":"https://openalex.org/G4522923412","display_name":null,"funder_award_id":"61871035","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6926355941","display_name":null,"funder_award_id":"62271051","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G986520376","display_name":null,"funder_award_id":"2020YFB1804901","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1849277567","https://openalex.org/W2299676078","https://openalex.org/W2339848418","https://openalex.org/W2900841156","https://openalex.org/W2916945904","https://openalex.org/W2924595251","https://openalex.org/W2963534994","https://openalex.org/W2981455641","https://openalex.org/W2981587265","https://openalex.org/W2998082633","https://openalex.org/W3000408141","https://openalex.org/W3001909141","https://openalex.org/W3010109240","https://openalex.org/W3010349995","https://openalex.org/W3014571735","https://openalex.org/W3047515578","https://openalex.org/W3065310332","https://openalex.org/W3116924652","https://openalex.org/W3118241766","https://openalex.org/W3132288679","https://openalex.org/W3135827483","https://openalex.org/W3137410534","https://openalex.org/W3138516171","https://openalex.org/W3152586515","https://openalex.org/W3167069639","https://openalex.org/W3169732994","https://openalex.org/W3186604907","https://openalex.org/W3216869961","https://openalex.org/W4207035043","https://openalex.org/W4212889502","https://openalex.org/W4285279075","https://openalex.org/W4285821826","https://openalex.org/W4286374893","https://openalex.org/W4312214315","https://openalex.org/W4312524677","https://openalex.org/W4312711737","https://openalex.org/W6729983426","https://openalex.org/W6767844734"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W1949722210","https://openalex.org/W2953613577","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Long-range":[0],"wide-area":[1],"network":[2,39,61,75,120,165,173],"(LoRaWAN)":[3],"is":[4,32,113,193],"a":[5,60,196],"widely":[6],"used":[7],"technology":[8],"in":[9,56,83,129,140,166,195,210],"the":[10,68,80,88,95,117,130,135,138,151,163,167,170,181,207,211,219,224,231,246],"Internet":[11],"of":[12,71,137,142,150,240],"Things":[13],"(IoT),":[14],"which":[15,115],"provides":[16],"long-range":[17],"(LoRa)":[18],"communication":[19],"with":[20,121,157,199],"low":[21],"power":[22],"consumption.":[23],"In":[24,42],"LoRaWAN,":[25],"an":[26,47,106],"accurate":[27],"path":[28],"loss":[29],"(PL)":[30],"model":[31,87],"essential":[33],"to":[34,66,86,103,133,217],"realize":[35],"link":[36],"budget":[37],"and":[38,127,145,160,175,223,242,248],"coverage":[40],"planning.":[41],"this":[43],"article,":[44],"we":[45,204],"present":[46],"artificial":[48],"intelligence":[49],"(AI)-enhanced":[50],"generalizable":[51],"scheme":[52,102,139,153,168,185,208,232],"for":[53,93],"PL":[54,69],"prediction":[55,97,143,189],"LoRaWAN.":[57],"We":[58,124],"propose":[59],"that":[62,206],"performs":[63],"corrective":[64],"adjustments":[65],"improve":[67],"estimates":[70],"empirical":[72],"models.":[73],"The":[74,148],"termed":[76],"STransRadio":[77,119,164],"benefits":[78],"from":[79],"self-attention":[81],"computation":[82],"Swin":[84],"Transformer":[85],"LoRa":[89],"correlation":[90],"about":[91],"propagation":[92],"enhancing":[94],"adjustment":[96],"accuracy.":[98],"To":[99],"generalize":[100],"our":[101,184],"new":[104,197,220],"scenarios,":[105],"multiscenario":[107],"deep":[108,176],"transfer":[109],"learning":[110],"(MDTL)":[111],"algorithm":[112],"proposed,":[114],"finetunes":[116],"pretrained":[118],"limited":[122,200],"data.":[123,202],"conduct":[125],"simulations":[126,159],"measurements":[128],"868-MHz":[131],"bands":[132],"assess":[134],"performance":[136],"terms":[141],"accuracy":[144],"generalization":[146],"ability.":[147],"effectiveness":[149],"proposed":[152],"has":[154],"been":[155],"verified":[156],"both":[158,218],"measurements.":[161],"Moreover,":[162],"outperforms":[169],"convolutional":[171],"neural":[172],"(CNN)":[174],"vision":[177],"transformer":[178],"(DeepViT).":[179],"With":[180,227],"MDTL":[182],"algorithm,":[183],"can":[186,214],"achieve":[187],"excellent":[188],"performances":[190],"when":[191],"it":[192],"applied":[194],"scenario":[198,213,222],"training":[201],"Furthermore,":[203],"verify":[205],"utilized":[209],"simulated":[212,221],"be":[215],"transferred":[216],"realistic":[225],"scenario.":[226],"only":[228],"100":[229],"samples,":[230],"achieves":[233],"root":[234],"mean":[235],"square":[236],"error":[237],"(RMSE)":[238],"values":[239],"7.27":[241],"5.96":[243],"dB":[244],"between":[245],"predicted":[247],"actual":[249],"PL,":[250],"respectively.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}