{"id":"https://openalex.org/W4399886201","doi":"https://doi.org/10.1109/icnc59896.2024.10556125","title":"Adaptive Channel Switching for Connected Vehicles under Extreme Weather Conditions: A Reinforcement Learning Based Approach","display_name":"Adaptive Channel Switching for Connected Vehicles under Extreme Weather Conditions: A Reinforcement Learning Based Approach","publication_year":2024,"publication_date":"2024-02-19","ids":{"openalex":"https://openalex.org/W4399886201","doi":"https://doi.org/10.1109/icnc59896.2024.10556125"},"language":"en","primary_location":{"id":"doi:10.1109/icnc59896.2024.10556125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc59896.2024.10556125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Computing, Networking and Communications (ICNC)","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/A5051353395","display_name":"Jian Liu","orcid":"https://orcid.org/0000-0001-8368-6228"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Liu","raw_affiliation_strings":["University of South Carolina,Dept. of Computer Science and Engineering,Columbia,South Carolina,USA","Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina,Dept. of Computer Science and Engineering,Columbia,South Carolina,USA","institution_ids":["https://openalex.org/I155781252"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056673342","display_name":"Chin\u2010Tser Huang","orcid":"https://orcid.org/0000-0003-3983-972X"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chin-Tser Huang","raw_affiliation_strings":["University of South Carolina,Dept. of Computer Science and Engineering,Columbia,South Carolina,USA","Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina,Dept. of Computer Science and Engineering,Columbia,South Carolina,USA","institution_ids":["https://openalex.org/I155781252"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":0.5682,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6511087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"451","last_page":"455"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10524","display_name":"Traffic control and management","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9970999956130981,"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.9850999712944031,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7576150894165039},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6068482398986816},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.505882203578949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2617661952972412},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.2125118374824524}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7576150894165039},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6068482398986816},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.505882203578949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2617661952972412},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2125118374824524}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc59896.2024.10556125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc59896.2024.10556125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Computing, Networking and Communications (ICNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2901103904","https://openalex.org/W3034140633","https://openalex.org/W3160554663","https://openalex.org/W4210293763","https://openalex.org/W4221083670","https://openalex.org/W4285118193","https://openalex.org/W4298857966","https://openalex.org/W4321441489","https://openalex.org/W4323767311","https://openalex.org/W4376461230","https://openalex.org/W4384519312","https://openalex.org/W4385062361","https://openalex.org/W6637967152"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4306904969","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2138720691","https://openalex.org/W2376932109","https://openalex.org/W4362501864","https://openalex.org/W2001405890"],"abstract_inverted_index":{"Due":[0],"to":[1,76,83,140,150],"the":[2,34,57,104,123,127,132,136,168,195],"enormous":[3],"volume":[4],"of":[5,36,56,60,199],"data":[6],"generated":[7],"by":[8],"connected":[9],"vehicles":[10],"(CVs),":[11],"future":[12],"vehicle-to-vehicle":[13],"(V2V)":[14],"commu-nications":[15],"will":[16],"require":[17],"a":[18,94,152,174],"high-throughput":[19],"communication":[20,85,156,201],"channel.":[21],"While":[22],"5G":[23,144,179],"millimeter-wave":[24],"(mmWave)":[25],"technology":[26],"generally":[27],"meets":[28],"these":[29,77],"requirements,":[30],"it":[31,138],"suffers":[32],"from":[33],"risk":[35],"performance":[37,86,198],"degradation":[38],"under":[39,88],"harsh":[40,206],"weather":[41,90,113,176],"conditions":[42],"such":[43],"as":[44,115,122],"heavy":[45],"rain,":[46],"snow":[47],"or":[48],"sand":[49],"storms.":[50],"This":[51],"decline":[52],"is":[53,73],"primarily":[54],"because":[55],"high":[58],"frequency":[59],"mmWave,":[61],"which":[62],"results":[63,187],"in":[64,126,205],"significant":[65],"propagation":[66],"attenuation":[67],"loss.":[68],"In":[69,80],"contrast,":[70],"4G":[71,147,182],"LTE":[72,148,183],"less":[74],"susceptible":[75],"environmental":[78,207],"challenges.":[79],"an":[81],"effort":[82],"improve":[84],"reliability":[87,196],"severe":[89],"conditions,":[91],"we":[92],"propose":[93],"Deep":[95],"Reinforcement":[96],"Learning":[97],"(DRL)-driven":[98],"approach.":[99],"Our":[100,160],"proposed":[101],"framework":[102,133,192],"utilizes":[103],"Received":[105],"Signal":[106],"Strength":[107],"Indicator":[108],"(RSSI),":[109],"short-term":[110],"throughput,":[111],"and":[112,146,154,181,197],"parameters":[114],"input":[116],"variables.":[117],"It":[118],"employs":[119],"cumulative":[120],"throughput":[121],"reward":[124],"metric":[125],"reinforcement":[128],"learning":[129],"process.":[130],"As":[131],"interacts":[134],"with":[135,173],"environment,":[137],"learns":[139],"dynamically":[141],"switch":[142],"between":[143,158],"mmWave":[145,180],"channels":[149],"maintain":[151],"robust":[153],"reliable":[155],"link":[157],"CVs.":[159],"approach":[161],"has":[162],"been":[163],"validated":[164],"through":[165],"simulations":[166],"using":[167],"ns-3":[169],"network":[170],"simulator,":[171],"enhanced":[172],"customized":[175],"model":[177],"for":[178],"channels.":[184],"The":[185],"simulation":[186],"confirm":[188],"that":[189],"our":[190],"DRL":[191],"substantially":[193],"improves":[194],"V2V":[200],"within":[202],"minutes,":[203],"even":[204],"conditions.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}