{"id":"https://openalex.org/W4401067245","doi":"https://doi.org/10.1145/3670105.3670180","title":"Wireless Link Quality Prediction Based on Temporal Convolutional Networks and Self-Attention Fusion","display_name":"Wireless Link Quality Prediction Based on Temporal Convolutional Networks and Self-Attention Fusion","publication_year":2024,"publication_date":"2024-05-24","ids":{"openalex":"https://openalex.org/W4401067245","doi":"https://doi.org/10.1145/3670105.3670180"},"language":"en","primary_location":{"id":"doi:10.1145/3670105.3670180","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3670105.3670180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things","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/A5016175338","display_name":"Y.H. Wang","orcid":"https://orcid.org/0009-0004-1151-5867"},"institutions":[{"id":"https://openalex.org/I927504317","display_name":"Nanchang Hangkong University","ror":"https://ror.org/0369pvp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I927504317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Wang","raw_affiliation_strings":["Nanchang Hangkong University, China"],"raw_orcid":"https://orcid.org/0009-0004-1151-5867","affiliations":[{"raw_affiliation_string":"Nanchang Hangkong University, China","institution_ids":["https://openalex.org/I927504317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091555427","display_name":"Linlan Liu","orcid":"https://orcid.org/0000-0002-6021-097X"},"institutions":[{"id":"https://openalex.org/I927504317","display_name":"Nanchang Hangkong University","ror":"https://ror.org/0369pvp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I927504317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linlan Liu","raw_affiliation_strings":["Nanchang Hangkong University, China"],"raw_orcid":"https://orcid.org/0000-0002-6021-097X","affiliations":[{"raw_affiliation_string":"Nanchang Hangkong University, China","institution_ids":["https://openalex.org/I927504317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I927504317"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"448","last_page":"453"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9733999967575073,"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/T13535","display_name":"Wireless Sensor Networks and IoT","score":0.9625999927520752,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.799168586730957},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.5517268180847168},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.550084114074707},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.5227035284042358},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.5039779543876648},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4498419761657715},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4482651650905609},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3678116500377655},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3235725462436676},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1993016004562378}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.799168586730957},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.5517268180847168},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.550084114074707},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.5227035284042358},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.5039779543876648},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4498419761657715},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4482651650905609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3678116500377655},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3235725462436676},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1993016004562378},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3670105.3670180","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3670105.3670180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W827024646","https://openalex.org/W1981276685","https://openalex.org/W2042971656","https://openalex.org/W2085093677","https://openalex.org/W2998862290","https://openalex.org/W3027841272","https://openalex.org/W3138795602","https://openalex.org/W3185910770","https://openalex.org/W4210640126","https://openalex.org/W4309915899","https://openalex.org/W4317931974","https://openalex.org/W4378228333","https://openalex.org/W6603242443"],"related_works":["https://openalex.org/W1518185400","https://openalex.org/W3200586296","https://openalex.org/W4230332972","https://openalex.org/W1998033311","https://openalex.org/W4247322236","https://openalex.org/W4293226380","https://openalex.org/W1762272577","https://openalex.org/W4206960768","https://openalex.org/W4229899156","https://openalex.org/W2158247860"],"abstract_inverted_index":{"Most":[0],"current":[1],"deep":[2],"learning-based":[3],"link":[4,12,23,50,109,127,136],"quality":[5,13,24,51,110,128,137],"prediction":[6,30,52,95,111,138,159],"methods":[7],"rely":[8],"on":[9,68,98,125],"statistically":[10],"derived":[11],"parameters":[14,58],"over":[15,59],"sampling":[16],"periods,":[17],"which":[18],"makes":[19,157],"short-term":[20],"correlations":[21],"in":[22,84,88],"difficult":[25],"to":[26,79],"capture,":[27],"and":[28,100],"the":[29,81,85,89,94,119,126,132,142,145,158],"task":[31],"often":[32],"requires":[33],"multiple":[34],"consecutive":[35],"probing":[36,60,146],"cycles,":[37,61],"increasing":[38],"energy":[39],"consumption.":[40],"To":[41],"this":[42,44,105,114],"end,":[43],"paper":[45],"proposes":[46],"a":[47,63,73,135,151],"method":[48,106],"for":[49],"using":[53],"sequences":[54],"of":[55,121,134,144],"physical":[56],"layer":[57],"including":[62],"Temporal":[64],"Convolutional":[65],"Network":[66],"based":[67],"improved":[69],"self-attention":[70,74],"(TCNS),":[71],"where":[72],"mechanism":[75],"(SAM)":[76],"is":[77,148],"used":[78],"capture":[80],"time":[82,90],"series":[83],"global":[86],"dependencies":[87],"series,":[91],"thus":[92],"improving":[93],"accuracy.":[96],"Experiments":[97],"laboratory-collected":[99],"public":[101],"datasets":[102],"show":[103],"that":[104,156],"outperforms":[107],"other":[108],"models.":[112],"In":[113,130],"paper,":[115],"we":[116],"also":[117],"investigate":[118],"effect":[120],"probe":[122],"cycle":[123,147],"length":[124,143],"prediction.":[129],"general,":[131],"accuracy":[133],"model":[139],"improves":[140],"as":[141],"lengthened.":[149],"Still,":[150],"certain":[152],"threshold":[153],"value":[154],"exists":[155],"performance":[160],"optimal.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
