{"id":"https://openalex.org/W2583476541","doi":"https://doi.org/10.1109/glocom.2016.7841515","title":"A Mixture Model for NLOS mmWave Interference Distribution","display_name":"A Mixture Model for NLOS mmWave Interference Distribution","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2583476541","doi":"https://doi.org/10.1109/glocom.2016.7841515","mag":"2583476541"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2016.7841515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2016.7841515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5053585693","display_name":"Hussain Elkotby","orcid":"https://orcid.org/0000-0002-7231-9171"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hussain Elkotby","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000020663","display_name":"Mai Vu","orcid":"https://orcid.org/0000-0003-4486-5500"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mai Vu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA","institution_ids":["https://openalex.org/I121934306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I121934306"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9993000030517578,"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/T12146","display_name":"Power Line Communications and Noise","score":0.9986000061035156,"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":0.5264766216278076},{"id":"https://openalex.org/keywords/beamforming","display_name":"Beamforming","score":0.5126692056655884},{"id":"https://openalex.org/keywords/inverse-gaussian-distribution","display_name":"Inverse Gaussian distribution","score":0.5033504366874695},{"id":"https://openalex.org/keywords/non-line-of-sight-propagation","display_name":"Non-line-of-sight propagation","score":0.49416616559028625},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.4738783538341522},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4334120750427246},{"id":"https://openalex.org/keywords/path-loss","display_name":"Path loss","score":0.426542729139328},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4163161814212799},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4129934012889862},{"id":"https://openalex.org/keywords/goodness-of-fit","display_name":"Goodness of fit","score":0.4105181097984314},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.33898985385894775},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32891684770584106},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29426783323287964},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.20786499977111816},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.1523716151714325},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12719041109085083},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.126848965883255},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.1259365975856781},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.10181766748428345}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5264766216278076},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.5126692056655884},{"id":"https://openalex.org/C132878287","wikidata":"https://www.wikidata.org/wiki/Q1671727","display_name":"Inverse Gaussian distribution","level":3,"score":0.5033504366874695},{"id":"https://openalex.org/C154910267","wikidata":"https://www.wikidata.org/wiki/Q1740982","display_name":"Non-line-of-sight propagation","level":3,"score":0.49416616559028625},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4738783538341522},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4334120750427246},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.426542729139328},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4163161814212799},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4129934012889862},{"id":"https://openalex.org/C132480984","wikidata":"https://www.wikidata.org/wiki/Q2034239","display_name":"Goodness of fit","level":2,"score":0.4105181097984314},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.33898985385894775},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32891684770584106},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29426783323287964},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.20786499977111816},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.1523716151714325},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12719041109085083},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.126848965883255},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.1259365975856781},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.10181766748428345}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/glocom.2016.7841515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2016.7841515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W588132785","https://openalex.org/W1971191880","https://openalex.org/W1991056455","https://openalex.org/W1997834106","https://openalex.org/W1999153524","https://openalex.org/W2010043948","https://openalex.org/W2025349323","https://openalex.org/W2031858701","https://openalex.org/W2039688938","https://openalex.org/W2049633694","https://openalex.org/W2103972037","https://openalex.org/W2104074482","https://openalex.org/W2138069220","https://openalex.org/W2154912975","https://openalex.org/W2186512298","https://openalex.org/W2964273844","https://openalex.org/W4233278384","https://openalex.org/W4237042686","https://openalex.org/W6685596670"],"related_works":["https://openalex.org/W4306950403","https://openalex.org/W22721396","https://openalex.org/W2253573804","https://openalex.org/W4300167711","https://openalex.org/W2806700348","https://openalex.org/W2963347964","https://openalex.org/W1994455657","https://openalex.org/W2168752585","https://openalex.org/W2982213597","https://openalex.org/W2988991102"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,12,26,90,102,114,136,147],"novel":[3],"model":[4,24,80,94,127],"for":[5,113],"the":[6,29,33,45,48,67,76,79,83,86,133],"NLOS":[7],"interference":[8],"power":[9],"distribution":[10,88],"in":[11,19],"cellular":[13,106,137],"system":[14],"employing":[15,139],"MIMO":[16],"beamforming":[17,143],"transmission":[18],"mmWave":[20,119],"spectrum.":[21],"The":[22,93,125],"proposed":[23],"is":[25,72,95,128],"mixture":[27,49,126],"of":[28,47,78,117,135],"Inverse":[30,34],"Gaussian":[31],"and":[32,62,108,122,144],"Weibull":[35],"distributions,":[36],"both":[37,58],"are":[38],"two-parameter":[39],"medium-to-heavy":[40],"tail":[41],"distributions.":[42],"To":[43],"estimate":[44],"parameters":[46],"model,":[50],"we":[51],"design":[52],"an":[53],"expectation":[54],"maximization":[55],"algorithm":[56],"using":[57],"analytical":[59],"moment":[60],"matching":[61],"maximum":[63],"likelihood":[64],"estimation.":[65],"Further,":[66],"information-theoretic":[68],"metric":[69],"relative":[70],"entropy":[71],"used":[73,130],"to":[74,89,131],"measure":[75],"goodness":[77],"by":[81],"capturing":[82],"distance":[84],"from":[85,101],"modeled":[87],"reference":[91],"one.":[92],"tested":[96],"against":[97],"simulation":[98],"data":[99],"obtained":[100],"stochastic":[103],"geometry":[104],"based":[105],"network":[107,138],"demonstrates":[109],"very":[110],"good":[111,148],"fit":[112,149],"wide":[115],"range":[116],"practical":[118],"path":[120],"loss":[121],"shadowing":[123],"values.":[124],"then":[129],"analyze":[132],"capacity":[134],"joint":[140],"dominant":[141],"mode":[142],"again":[145],"confirms":[146],"with":[150],"simulation.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}