{"id":"https://openalex.org/W2886377746","doi":"https://doi.org/10.1109/icc.2018.8422582","title":"Characterization of V2V Coverage in a Network of Roads Modeled as Poisson Line Process","display_name":"Characterization of V2V Coverage in a Network of Roads Modeled as Poisson Line Process","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2886377746","doi":"https://doi.org/10.1109/icc.2018.8422582","mag":"2886377746"},"language":"en","primary_location":{"id":"doi:10.1109/icc.2018.8422582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2018.8422582","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Communications (ICC)","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/A5019166936","display_name":"Vishnu Vardhan Chetlur","orcid":"https://orcid.org/0000-0001-6526-5833"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vishnu Vardhan Chetlur","raw_affiliation_strings":["Department of ECE, Virginia Tech, Blacksburg, VA"],"affiliations":[{"raw_affiliation_string":"Department of ECE, Virginia Tech, Blacksburg, VA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054535609","display_name":"Sayantan Guha","orcid":"https://orcid.org/0000-0002-5640-638X"},"institutions":[{"id":"https://openalex.org/I4210135298","display_name":"Jensen Hughes (United States)","ror":"https://ror.org/033wnbm63","country_code":"US","type":"company","lineage":["https://openalex.org/I4210135298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sayantan Guha","raw_affiliation_strings":["Hughes Network Systems, LLC, Germantown, MD, USA"],"affiliations":[{"raw_affiliation_string":"Hughes Network Systems, LLC, Germantown, MD, USA","institution_ids":["https://openalex.org/I4210135298"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064063671","display_name":"Harpreet S. Dhillon","orcid":"https://orcid.org/0000-0003-2829-9449"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Harpreet S. Dhillon","raw_affiliation_strings":["Department of ECE, Virginia Tech, Blacksburg, VA"],"affiliations":[{"raw_affiliation_string":"Department of ECE, Virginia Tech, Blacksburg, VA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019166936"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":0.515,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.67371461,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9998999834060669,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9998999834060669,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9750000238418579,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/poisson-point-process","display_name":"Poisson point process","score":0.6877008676528931},{"id":"https://openalex.org/keywords/stochastic-geometry","display_name":"Stochastic geometry","score":0.6828171014785767},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6800927519798279},{"id":"https://openalex.org/keywords/nakagami-distribution","display_name":"Nakagami distribution","score":0.6530798077583313},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5978438258171082},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.5742590427398682},{"id":"https://openalex.org/keywords/fading","display_name":"Fading","score":0.5542920827865601},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.5498131513595581},{"id":"https://openalex.org/keywords/coverage-probability","display_name":"Coverage probability","score":0.5447897911071777},{"id":"https://openalex.org/keywords/cox-process","display_name":"Cox process","score":0.5350905656814575},{"id":"https://openalex.org/keywords/beamforming","display_name":"Beamforming","score":0.5260103940963745},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.43455690145492554},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.4233383536338806},{"id":"https://openalex.org/keywords/point-process","display_name":"Point process","score":0.4162670373916626},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.31789490580558777},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26941215991973877},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.255459189414978},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16490983963012695},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16441529989242554},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.10543256998062134},{"id":"https://openalex.org/keywords/poisson-process","display_name":"Poisson process","score":0.0991925597190857}],"concepts":[{"id":"https://openalex.org/C16757284","wikidata":"https://www.wikidata.org/wiki/Q1145117","display_name":"Poisson point process","level":3,"score":0.6877008676528931},{"id":"https://openalex.org/C31243852","wikidata":"https://www.wikidata.org/wiki/Q1666739","display_name":"Stochastic geometry","level":2,"score":0.6828171014785767},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6800927519798279},{"id":"https://openalex.org/C115098869","wikidata":"https://www.wikidata.org/wiki/Q3258347","display_name":"Nakagami distribution","level":4,"score":0.6530798077583313},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5978438258171082},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.5742590427398682},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.5542920827865601},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.5498131513595581},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.5447897911071777},{"id":"https://openalex.org/C155051063","wikidata":"https://www.wikidata.org/wiki/Q1290919","display_name":"Cox process","level":4,"score":0.5350905656814575},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.5260103940963745},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.43455690145492554},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.4233383536338806},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.4162670373916626},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31789490580558777},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26941215991973877},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.255459189414978},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16490983963012695},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16441529989242554},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.10543256998062134},{"id":"https://openalex.org/C166144826","wikidata":"https://www.wikidata.org/wiki/Q1145117","display_name":"Poisson process","level":3,"score":0.0991925597190857},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"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/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc.2018.8422582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2018.8422582","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.699999988079071,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W631335369","https://openalex.org/W2041078943","https://openalex.org/W2107846430","https://openalex.org/W2118166339","https://openalex.org/W2168428394","https://openalex.org/W2289685866","https://openalex.org/W2338693689","https://openalex.org/W2577541407","https://openalex.org/W2963218636","https://openalex.org/W2963692345","https://openalex.org/W6660445384"],"related_works":["https://openalex.org/W2059270103","https://openalex.org/W1605884181","https://openalex.org/W2949860582","https://openalex.org/W1985655499","https://openalex.org/W2406192116","https://openalex.org/W1674302630","https://openalex.org/W1573068833","https://openalex.org/W2947805744","https://openalex.org/W2314349990","https://openalex.org/W4389820878"],"abstract_inverted_index":{"In":[0,17,47,62],"this":[1,48],"paper,":[2],"we":[3,19,54,79,107,162],"study":[4],"the":[5,21,32,56,66,82,86,94,109,124,127,130,134,139,144,147,153,176],"coverage":[6,114,168,187],"performance":[7,57,115],"of":[8,23,34,51,58,126,146,149,172,175,178,192],"vehicle-to-vehicle":[9],"(V2V)":[10],"communication":[11],"using":[12,25,105],"tools":[13],"from":[14],"stochastic":[15],"geometry.":[16],"particular,":[18,63],"model":[20],"layout":[22],"roads":[24],"a":[26,41,59,91,98,190],"Poisson":[27,43],"line":[28,39,197],"process":[29,45,50],"(PLP)":[30],"and":[31,133,143,199],"locations":[33],"vehicular":[35,52],"nodes":[36,68],"on":[37,93,152],"each":[38],"as":[40,189],"1D":[42],"point":[44],"(PPP).":[46],"Cox":[49],"nodes,":[53],"analyze":[55],"typical":[60,87,131],"receiver.":[61],"assuming":[64],"that":[65],"transmitting":[67],"employ":[69],"beamforming":[70],"to":[71,90],"maximize":[72],"signal":[73,141],"power":[74,101],"along":[75],"their":[76],"own":[77],"roads,":[78],"first":[80],"determine":[81],"probability":[83,169,188],"with":[84],"which":[85,106],"receiver":[88,132],"connects":[89],"node":[92,136,155,200],"same":[95],"road":[96],"under":[97,116],"maximum":[99],"average":[100],"based":[102,113],"association":[103],"scheme,":[104],"characterize":[108,138,157],"signal-to-interference":[110],"ratio":[111],"(SIR)":[112],"Nakagami-m":[117],"fading.":[118],"Key":[119],"intermediate":[120],"steps":[121],"involve":[122],"deriving":[123],"distribution":[125,145,177],"distance":[128],"between":[129],"serving":[135,154],"(to":[137,156],"desired":[140],"power)":[142],"number":[148],"lines":[150],"conditioned":[151],"interference).":[158],"Using":[159],"these":[160],"results,":[161],"derive":[163],"an":[164],"exact":[165],"expression":[166],"for":[167],"in":[170,186],"terms":[171],"Laplace":[173],"transform":[174],"interference":[179],"power.":[180],"Our":[181],"analysis":[182],"reveals":[183],"useful":[184],"trends":[185],"function":[191],"key":[193],"network":[194],"parameters,":[195],"namely,":[196],"density":[198],"density.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}