{"id":"https://openalex.org/W3122361133","doi":"https://doi.org/10.1109/globecom42002.2020.9322189","title":"Gaussian Models for CSI Fingerprinting in Practical Indoor Environment Identification","display_name":"Gaussian Models for CSI Fingerprinting in Practical Indoor Environment Identification","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3122361133","doi":"https://doi.org/10.1109/globecom42002.2020.9322189","mag":"3122361133"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9322189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322189","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://ira.lib.polyu.edu.hk/bitstream/10397/107118/1/Rocamora_Gaussian_Models_Csi.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070029003","display_name":"Josyl Mariela B. Rocamora","orcid":null},"institutions":[{"id":"https://openalex.org/I82904351","display_name":"University of Santo Tomas","ror":"https://ror.org/00d25af97","country_code":"PH","type":"education","lineage":["https://openalex.org/I82904351"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Josyl Mariela Rocamora","raw_affiliation_strings":["University of Santo Tomas, Manila, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Santo Tomas, Manila, Philippines","institution_ids":["https://openalex.org/I82904351"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103345095","display_name":"I.S.K. Ho","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"IvanWang-Hei Ho","raw_affiliation_strings":["The Hong Kong Polytechnic university"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic university","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068768998","display_name":"Man\u2010Wai Mak","orcid":"https://orcid.org/0000-0001-8854-3760"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Man-Wai Mak","raw_affiliation_strings":["The Hong Kong Polytechnic university"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic university","institution_ids":["https://openalex.org/I14243506"]}]}],"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":6,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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/T10860","display_name":"Speech and Audio Processing","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.995199978351593,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7953052520751953},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.7528249621391296},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.6170942783355713},{"id":"https://openalex.org/keywords/channel-state-information","display_name":"Channel state information","score":0.5761570334434509},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5276264548301697},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5224862694740295},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.48599323630332947},{"id":"https://openalex.org/keywords/gaussian-network-model","display_name":"Gaussian network model","score":0.463577538728714},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4508407711982727},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4347408711910248},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4203430414199829},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3022528886795044},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1407146453857422}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7953052520751953},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.7528249621391296},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.6170942783355713},{"id":"https://openalex.org/C148063708","wikidata":"https://www.wikidata.org/wiki/Q5072511","display_name":"Channel state information","level":3,"score":0.5761570334434509},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5276264548301697},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5224862694740295},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.48599323630332947},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.463577538728714},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4508407711982727},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4347408711910248},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4203430414199829},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3022528886795044},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1407146453857422},{"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/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/globecom42002.2020.9322189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322189","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:ira.lib.polyu.edu.hk:10397/107118","is_oa":true,"landing_page_url":"http://hdl.handle.net/10397/107118","pdf_url":"http://ira.lib.polyu.edu.hk/bitstream/10397/107118/1/Rocamora_Gaussian_Models_Csi.pdf","source":{"id":"https://openalex.org/S4306400205","display_name":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I14243506","host_organization_name":"Hong Kong Polytechnic University","host_organization_lineage":["https://openalex.org/I14243506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"}],"best_oa_location":{"id":"pmh:oai:ira.lib.polyu.edu.hk:10397/107118","is_oa":true,"landing_page_url":"http://hdl.handle.net/10397/107118","pdf_url":"http://ira.lib.polyu.edu.hk/bitstream/10397/107118/1/Rocamora_Gaussian_Models_Csi.pdf","source":{"id":"https://openalex.org/S4306400205","display_name":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I14243506","host_organization_name":"Hong Kong Polytechnic University","host_organization_lineage":["https://openalex.org/I14243506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320307285","display_name":"Impact Fund","ror":"https://ror.org/00jb20j87"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W3122361133.pdf"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1531910981","https://openalex.org/W2051376734","https://openalex.org/W2111986491","https://openalex.org/W2114856874","https://openalex.org/W2142136129","https://openalex.org/W2170339786","https://openalex.org/W2501648206","https://openalex.org/W2551222976","https://openalex.org/W2552265749","https://openalex.org/W2558152194","https://openalex.org/W2587657017","https://openalex.org/W2726539084","https://openalex.org/W2756489842","https://openalex.org/W2762342365","https://openalex.org/W2773310738","https://openalex.org/W2778889146","https://openalex.org/W2787083943","https://openalex.org/W2792312025","https://openalex.org/W2794618457","https://openalex.org/W2810394422","https://openalex.org/W2898138808","https://openalex.org/W2947490884","https://openalex.org/W2952065976","https://openalex.org/W2952518356","https://openalex.org/W2967150540","https://openalex.org/W2979563830","https://openalex.org/W3028154864","https://openalex.org/W3105513546","https://openalex.org/W4212863985","https://openalex.org/W6729633912","https://openalex.org/W6729790184"],"related_works":["https://openalex.org/W2350507978","https://openalex.org/W2016260880","https://openalex.org/W3162483426","https://openalex.org/W2388204628","https://openalex.org/W2386749094","https://openalex.org/W2055782493","https://openalex.org/W2194875745","https://openalex.org/W1505666352","https://openalex.org/W2806332051","https://openalex.org/W1969050958"],"abstract_inverted_index":{"It":[0],"is":[1],"not":[2],"uncommon":[3],"to":[4,13,52,107,117,163,171],"experience":[5],"highly":[6],"dynamic":[7,54],"channels":[8],"in":[9,78,85,97,138],"indoor":[10,46,80],"environments":[11,81],"due":[12],"time-varying":[14],"signals":[15],"as":[16,18],"well":[17],"moving":[19],"reflectors":[20],"and":[21,39,48,62,75,157,173,183],"scatterers.":[22],"This":[23],"greatly":[24],"affects":[25],"the":[26,98,109,127,135,139,146,178,184],"performance":[27,175],"of":[28,105,152],"wireless":[29,110,140],"sensing":[30],"systems":[31,89,101],"that":[32,71,145],"use":[33,102],"received":[34],"signal":[35],"strength":[36,181],"indicator":[37],"(RSSI)":[38],"channel":[40,55,128],"state":[41],"information":[42,125],"(CSI)":[43],"fingerprints":[44],"for":[45],"positioning":[47],"event":[49],"detection.":[50],"Solutions":[51],"this":[53,86],"problem":[56],"often":[57],"involve":[58],"laborintensive":[59],"database":[60],"maintenance":[61],"customized":[63],"hardware.":[64],"With":[65],"this,":[66],"we":[67,132],"present":[68],"Gaussian":[69,91,115,147],"models":[70,92],"can":[72,133],"withstand":[73],"temporal":[74],"environmental":[76],"dynamics":[77,129],"practical":[79],"using":[82,113],"off-the-shelf":[83],"devices":[84],"paper.":[87],"Although":[88],"employing":[90],"have":[93],"been":[94],"previously":[95],"proposed":[96],"literature,":[99],"most":[100],"RSSI":[103],"instead":[104],"CSI":[106,119],"represent":[108],"channel.":[111],"By":[112],"a":[114],"distribution":[116],"model":[118],"fingerprints,":[120],"which":[121],"offer":[122],"more":[123],"abundant":[124],"regarding":[126],"than":[130,154],"RSSI,":[131],"exploit":[134],"variance":[136],"inherent":[137],"channels.":[141],"Our":[142],"experiments":[143],"demonstrate":[144],"classifier":[148],"incurs":[149],"minimal":[150],"delay":[151],"less":[153],"4":[155],"seconds":[156],"achieves":[158,169],"high":[159],"classification":[160],"accuracy":[161],"compared":[162],"other":[164],"techniques.":[165],"In":[166],"particular,":[167],"it":[168],"up":[170],"50%":[172],"150%":[174],"improvement":[176],"over":[177],"time-reversal":[179],"resonating":[180],"(TRRS)":[182],"support":[185],"vector":[186],"machines":[187],"(SVM)":[188],"methods,":[189],"respectively.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
