{"id":"https://openalex.org/W4394841759","doi":"https://doi.org/10.1109/tcomm.2024.3388506","title":"Understanding the Performance of Learning Precoding Policies With Graph and Convolutional Neural Networks","display_name":"Understanding the Performance of Learning Precoding Policies With Graph and Convolutional Neural Networks","publication_year":2024,"publication_date":"2024-04-16","ids":{"openalex":"https://openalex.org/W4394841759","doi":"https://doi.org/10.1109/tcomm.2024.3388506"},"language":"en","primary_location":{"id":"doi:10.1109/tcomm.2024.3388506","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tcomm.2024.3388506","pdf_url":null,"source":{"id":"https://openalex.org/S196647941","display_name":"IEEE Transactions on Communications","issn_l":"0090-6778","issn":["0090-6778","1558-0857"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Communications","raw_type":"journal-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/A5041458795","display_name":"Baichuan Zhao","orcid":"https://orcid.org/0000-0002-5654-5136"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Baichuan Zhao","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038465404","display_name":"Jia Guo","orcid":"https://orcid.org/0000-0002-2134-7367"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Guo","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082950698","display_name":"Chenyang Yang","orcid":"https://orcid.org/0000-0003-0058-0765"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyang Yang","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5041458795"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":3.8983,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.94028917,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"72","issue":"9","first_page":"5657","last_page":"5673"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.7113000154495239,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.7113000154495239,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.633899986743927,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/precoding","display_name":"Precoding","score":0.8043562173843384},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6717845797538757},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5538566708564758},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5396355390548706},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.488753080368042},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.456502765417099},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4284258186817169},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3100842535495758},{"id":"https://openalex.org/keywords/mimo","display_name":"MIMO","score":0.29252681136131287},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.25098341703414917},{"id":"https://openalex.org/keywords/beamforming","display_name":"Beamforming","score":0.100362628698349}],"concepts":[{"id":"https://openalex.org/C160562895","wikidata":"https://www.wikidata.org/wiki/Q7239557","display_name":"Precoding","level":4,"score":0.8043562173843384},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6717845797538757},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5538566708564758},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5396355390548706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.488753080368042},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.456502765417099},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4284258186817169},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3100842535495758},{"id":"https://openalex.org/C207987634","wikidata":"https://www.wikidata.org/wiki/Q176862","display_name":"MIMO","level":3,"score":0.29252681136131287},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.25098341703414917},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.100362628698349}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcomm.2024.3388506","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tcomm.2024.3388506","pdf_url":null,"source":{"id":"https://openalex.org/S196647941","display_name":"IEEE Transactions on Communications","issn_l":"0090-6778","issn":["0090-6778","1558-0857"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1766761911","display_name":null,"funder_award_id":"62271024","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2792895487","display_name":null,"funder_award_id":"2022YFB2902002","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1520252399","https://openalex.org/W1970830346","https://openalex.org/W2016852016","https://openalex.org/W2072599023","https://openalex.org/W2103972037","https://openalex.org/W2127401968","https://openalex.org/W2137983211","https://openalex.org/W2143927285","https://openalex.org/W2147601077","https://openalex.org/W2147800946","https://openalex.org/W2162841150","https://openalex.org/W2162888803","https://openalex.org/W2164536085","https://openalex.org/W2528305538","https://openalex.org/W2800415562","https://openalex.org/W2805516822","https://openalex.org/W2908498346","https://openalex.org/W2949883708","https://openalex.org/W2962883549","https://openalex.org/W2970834748","https://openalex.org/W2971598376","https://openalex.org/W2979038370","https://openalex.org/W2986661129","https://openalex.org/W3037151850","https://openalex.org/W3040589146","https://openalex.org/W3086007150","https://openalex.org/W3096252532","https://openalex.org/W3099684761","https://openalex.org/W3100120649","https://openalex.org/W3102260762","https://openalex.org/W3112949451","https://openalex.org/W3129331719","https://openalex.org/W3131889149","https://openalex.org/W3134253720","https://openalex.org/W3160036209","https://openalex.org/W3189854358","https://openalex.org/W3193952356","https://openalex.org/W3194568054","https://openalex.org/W3194828218","https://openalex.org/W3200409881","https://openalex.org/W3203938711","https://openalex.org/W3212630591","https://openalex.org/W4221150253","https://openalex.org/W4236362309","https://openalex.org/W4249716558","https://openalex.org/W4280508843","https://openalex.org/W4285505691","https://openalex.org/W4288777480","https://openalex.org/W4299649195","https://openalex.org/W4320713306","https://openalex.org/W4320800818","https://openalex.org/W4386038394","https://openalex.org/W6677356779","https://openalex.org/W6739659843","https://openalex.org/W6748600614","https://openalex.org/W6749406891","https://openalex.org/W6751002639","https://openalex.org/W6751796012","https://openalex.org/W6758857762","https://openalex.org/W6768783436","https://openalex.org/W6774527605","https://openalex.org/W6797907005","https://openalex.org/W6843907867"],"related_works":["https://openalex.org/W4281688526","https://openalex.org/W2152088989","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Learning-based":[0],"precoding":[1,36,54,62,94,143],"has":[2],"been":[3],"shown":[4],"able":[5],"to":[6,18,46,92,100,147,167],"be":[7,114,148],"implemented":[8],"in":[9,138,188],"real-time,":[10],"jointly":[11],"optimized":[12],"with":[13,85],"channel":[14],"acquisition,":[15],"and":[16,27,96,118,129,153,180],"robust":[17],"imperfect":[19],"channels.":[20],"Nonetheless,":[21],"existing":[22],"works":[23],"rarely":[24],"explain":[25],"when":[26],"why":[28],"a":[29,80,127,134,151],"deep":[30],"neural":[31,82,105],"network":[32,83],"(DNN)":[33],"for":[34,58],"learning":[35,49,111],"policy":[37,128,144],"can":[38,113],"perform":[39],"well.":[40],"In":[41],"this":[42,76],"paper,":[43],"we":[44,78,136],"strive":[45],"understand":[47],"the":[48,60,93,101,110,124,130,142,155,169,174,182],"performance":[50,112],"by":[51,150,159],"taking":[52],"baseband":[53],"as":[55,68],"an":[56],"example,":[57],"which":[59,121,139],"optimal":[61],"matrices":[63],"of":[64,126,133,173,184,190],"several":[65],"problems":[66],"such":[67],"sum":[69],"rate":[70],"maximization":[71],"have":[72],"mathematical":[73],"structure.":[74],"Toward":[75],"goal,":[77],"design":[79],"graph":[81],"(GNN)":[84],"edge-update":[86],"mechanism,":[87],"whose":[88],"inductive":[89,131,156],"bias":[90,132],"matches":[91],"policy,":[95],"analyze":[97,137],"its":[98],"connection":[99],"commonly":[102],"used":[103],"convolutional":[104],"networks":[106],"(CNNs).":[107],"Noticing":[108],"that":[109],"decomposed":[115],"into":[116],"approximation":[117],"estimation":[119,163,170],"errors,":[120],"depend":[122],"on":[123],"smoothness":[125],"DNN,":[135],"system":[140],"settings":[141],"is":[145],"harder":[146],"approximated":[149],"DNN":[152],"how":[154],"biases":[157],"introduced":[158],"parameter":[160],"sharing":[161],"affect":[162],"errors.":[164],"We":[165],"proceed":[166],"derive":[168],"error":[171],"bounds":[172],"DNNs.":[175],"Simulations":[176],"validate":[177],"our":[178],"analyses":[179],"verify":[181],"gain":[183],"GNN":[185],"over":[186],"CNNs":[187],"terms":[189],"reducing":[191],"sample":[192],"complexity.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}