{"id":"https://openalex.org/W4210803956","doi":"https://doi.org/10.1109/globecom46510.2021.9685056","title":"Performance Optimization for Semantic Communications: An Attention-based Learning Approach","display_name":"Performance Optimization for Semantic Communications: An Attention-based Learning Approach","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4210803956","doi":"https://doi.org/10.1109/globecom46510.2021.9685056"},"language":"en","primary_location":{"id":"doi:10.1109/globecom46510.2021.9685056","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom46510.2021.9685056","pdf_url":null,"source":{"id":"https://openalex.org/S4363607714","display_name":"2021 IEEE Global Communications Conference (GLOBECOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Global Communications Conference (GLOBECOM)","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/A5101796015","display_name":"Yining Wang","orcid":"https://orcid.org/0000-0003-2353-3400"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yining Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Network,Beijing,China","Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Network,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072241033","display_name":"Mingzhe Chen","orcid":"https://orcid.org/0000-0003-2570-703X"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingzhe Chen","raw_affiliation_strings":["Princeton University,Department of Electrical and Computer Engineering,Princeton,NJ,USA"],"affiliations":[{"raw_affiliation_string":"Princeton University,Department of Electrical and Computer Engineering,Princeton,NJ,USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024108653","display_name":"Walid Saad","orcid":"https://orcid.org/0000-0003-2247-2458"},"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":"Walid Saad","raw_affiliation_strings":["Wireless@ VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech,Blacksburg,VA,USA"],"affiliations":[{"raw_affiliation_string":"Wireless@ VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech,Blacksburg,VA,USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002713029","display_name":"Tao Luo","orcid":"https://orcid.org/0000-0003-4870-5942"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Luo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Network,Beijing,China","Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Network,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009164482","display_name":"Shuguang Cui","orcid":"https://orcid.org/0000-0003-2608-775X"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuguang Cui","raw_affiliation_strings":["Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute, the Chinese University of Hong Kong,Shenzhen,China","Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute, the Chinese University of Hong Kong, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute, the Chinese University of Hong Kong,Shenzhen,China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute, the Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924","https://openalex.org/I4210099586"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042307561","display_name":"H. Vincent Poor","orcid":"https://orcid.org/0000-0002-2062-131X"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"H. Vincent Poor","raw_affiliation_strings":["Princeton University,Department of Electrical and Computer Engineering,Princeton,NJ,USA"],"affiliations":[{"raw_affiliation_string":"Princeton University,Department of Electrical and Computer Engineering,Princeton,NJ,USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101796015"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":5.2777,"has_fulltext":false,"cited_by_count":58,"citation_normalized_percentile":{"value":0.96756403,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9764999747276306,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9692000150680542,"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":0.766332745552063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.505549430847168},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.40507179498672485}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.766332745552063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.505549430847168},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40507179498672485}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom46510.2021.9685056","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom46510.2021.9685056","pdf_url":null,"source":{"id":"https://openalex.org/S4363607714","display_name":"2021 IEEE Global Communications Conference (GLOBECOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2015320000","display_name":null,"funder_award_id":"62171047","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2123301721","https://openalex.org/W2515462165","https://openalex.org/W2801930304","https://openalex.org/W2893836673","https://openalex.org/W2954922414","https://openalex.org/W2963718112","https://openalex.org/W2981096252","https://openalex.org/W3003265726","https://openalex.org/W3036851434","https://openalex.org/W3090615085","https://openalex.org/W3113050802","https://openalex.org/W3143834386","https://openalex.org/W3172394904","https://openalex.org/W3180974649","https://openalex.org/W3198139956","https://openalex.org/W3203789000","https://openalex.org/W4288375838","https://openalex.org/W4385245566","https://openalex.org/W6678262379","https://openalex.org/W6739901393","https://openalex.org/W6754364366","https://openalex.org/W6898505805"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"In":[0,12],"this":[1,178],"paper,":[2],"a":[3,16,39,50,67,81,180,215,259,268],"semantic":[4,22,34,46,53,58,78,84,91,112,140,171,206,226,238,276],"communication":[5,79,239,270],"framework":[6,240],"is":[7,36,99,147,155,192],"proposed":[8,14,195,237],"for":[9,134],"wireless":[10,103],"networks.":[11],"the":[13,21,45,57,63,73,76,90,96,106,121,126,158,163,169,189,199,205,218,222,225,229,236,243,248,263],"framework,":[15],"base":[17],"station":[18],"(BS)":[19],"extracts":[20],"information":[23,35,47,113,141,172,207,227],"from":[24],"textual":[25],"data,":[26],"and,":[27],"transmits":[28],"it":[29],"to":[30,102,114,119,142,156,173,251,255,267],"each":[31,60,115,135,202],"user.":[32],"This":[33,145],"modeled":[37],"by":[38,161,253],"knowledge":[40],"graph":[41],"(KG)":[42],"and":[43,93,137,167,212,228,257],"hence,":[44],"consists":[48],"of":[49,52,75,83,95,201,221,245],"set":[51],"triples.":[54],"After":[55],"receiving":[56],"information,":[59],"user":[61,116,136],"recovers":[62],"original":[64],"text":[65,98],"using":[66,208],"graph-to-text":[68],"generation":[69],"model.":[70],"To":[71,176],"measure":[72],"performance":[74],"studied":[77],"system,":[80],"metric":[82],"similarity":[85],"(MSS)":[86],"that":[87,235,247,272],"jointly":[88],"captures":[89],"accuracy":[92],"completeness":[94],"recovered":[97],"proposed.":[100,193],"Due":[101],"resource":[104,132,164],"limitations,":[105],"BS":[107,127,249],"can":[108,197,241],"only":[109],"transmit":[110,252],"partial":[111,139,170],"so":[117],"as":[118,149],"satisfy":[120],"transmission":[122],"delay":[123],"constraint.":[124],"Hence,":[125],"must":[128],"select":[129],"an":[130,150,209],"appropriate":[131],"block":[133],"determine":[138],"be":[143,174],"transmitted.":[144,175],"problem":[146,152],"formulated":[148],"optimization":[151],"whose":[153],"goal":[154],"maximize":[157],"total":[159,230,264],"MSS":[160,265],"optimizing":[162],"allocation":[165],"policy":[166,181],"determining":[168],"solve":[177],"problem,":[179],"gradient-based":[182],"reinforcement":[183],"learning":[184],"(RL)":[185],"algorithm":[186,196],"integrated":[187],"with":[188],"attention":[190,210],"network":[191,211,271],"The":[194],"evaluate":[198],"importance":[200,219],"triple":[203],"in":[204,224,262],"then,":[213],"build":[214],"relationship":[216],"between":[217],"distribution":[220],"triples":[223],"MSS.":[231],"Simulation":[232],"results":[233],"demonstrate":[234],"reduce":[242],"size":[244],"data":[246],"needs":[250],"up":[254],"46%":[256],"yield":[258],"two-fold":[260],"improvement":[261],"compared":[266],"standard":[269],"does":[273],"not":[274],"consider":[275],"communications.":[277]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}