{"id":"https://openalex.org/W4392158279","doi":"https://doi.org/10.1109/globecom54140.2023.10436861","title":"Learning Precoding Policy with Inductive Biases: Graph Neural Networks or Meta-Learning?","display_name":"Learning Precoding Policy with Inductive Biases: Graph Neural Networks or Meta-Learning?","publication_year":2023,"publication_date":"2023-12-04","ids":{"openalex":"https://openalex.org/W4392158279","doi":"https://doi.org/10.1109/globecom54140.2023.10436861"},"language":"en","primary_location":{"id":"doi:10.1109/globecom54140.2023.10436861","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom54140.2023.10436861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2023 - 2023 IEEE Global Communications Conference","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/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":["Beihang University,Beijing,China","Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025038200","display_name":"Yang Ma","orcid":null},"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":"Yang Ma","raw_affiliation_strings":["Beihang University,Beijing,China","Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100621604","display_name":"Jiajun Wu","orcid":"https://orcid.org/0000-0002-2813-3249"},"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":"Jiajun Wu","raw_affiliation_strings":["Beihang University,Beijing,China","Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"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":["Beihang University,Beijing,China","Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041458795"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.5237,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.7336219,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4835","last_page":"4840"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9980000257492065,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9980000257492065,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9940999746322632,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9907000064849854,"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/computer-science","display_name":"Computer science","score":0.6861772537231445},{"id":"https://openalex.org/keywords/precoding","display_name":"Precoding","score":0.6387362480163574},{"id":"https://openalex.org/keywords/inductive-bias","display_name":"Inductive bias","score":0.6172537803649902},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5229367017745972},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5132884383201599},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4954177141189575},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37454694509506226},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2965810298919678},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.2263799011707306},{"id":"https://openalex.org/keywords/mimo","display_name":"MIMO","score":0.14263924956321716},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13257023692131042},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11341395974159241}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6861772537231445},{"id":"https://openalex.org/C160562895","wikidata":"https://www.wikidata.org/wiki/Q7239557","display_name":"Precoding","level":4,"score":0.6387362480163574},{"id":"https://openalex.org/C197352929","wikidata":"https://www.wikidata.org/wiki/Q1074074","display_name":"Inductive bias","level":4,"score":0.6172537803649902},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5229367017745972},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5132884383201599},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4954177141189575},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37454694509506226},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2965810298919678},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.2263799011707306},{"id":"https://openalex.org/C207987634","wikidata":"https://www.wikidata.org/wiki/Q176862","display_name":"MIMO","level":3,"score":0.14263924956321716},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13257023692131042},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11341395974159241},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom54140.2023.10436861","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom54140.2023.10436861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2023 - 2023 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4000000059604645,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"}],"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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1970830346","https://openalex.org/W2072599023","https://openalex.org/W2162888803","https://openalex.org/W3100120649","https://openalex.org/W3163842339","https://openalex.org/W3180996795","https://openalex.org/W4280508843","https://openalex.org/W4290717453","https://openalex.org/W4294811252","https://openalex.org/W4302363874","https://openalex.org/W4312288512","https://openalex.org/W4317419063","https://openalex.org/W6736057607","https://openalex.org/W6752796717"],"related_works":["https://openalex.org/W2152088989","https://openalex.org/W2756419127","https://openalex.org/W4366493479","https://openalex.org/W2905893439","https://openalex.org/W3135374966","https://openalex.org/W2554517556","https://openalex.org/W2953205382","https://openalex.org/W1994906402","https://openalex.org/W4285231928","https://openalex.org/W3180149110"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"has":[2],"been":[3],"introduced":[4],"to":[5,51,53,117,130,162,183],"optimize":[6],"wireless":[7,37,111],"policies":[8,112],"such":[9],"as":[10,159],"precoding":[11,157,180],"for":[12,66,147,179],"enabling":[13],"real-time":[14],"implementation.":[15],"Yet":[16],"prevalent":[17],"studies":[18],"assume":[19],"that":[20,169],"training":[21,64,194],"and":[22,113,177],"test":[23],"samples":[24,65,146],"are":[25,172],"drawn":[26],"from":[27,192],"the":[28,57,101,119,132,154,164,170,187,193],"same":[29],"distribution,":[30],"which":[31,77,107,135],"is":[32,141],"not":[33],"true":[34],"in":[35,86,110,144],"dynamic":[36],"environments.":[38],"As":[39],"a":[40,42,123],"result,":[41],"well-trained":[43],"deep":[44],"neural":[45,91],"network":[46],"(DNN)":[47],"may":[48],"require":[49],"retraining":[50],"adapt":[52,182],"new":[54,184],"environments,":[55],"incurring":[56],"overhead":[58],"of":[59,122,137,189],"data":[60],"collection.":[61],"The":[62],"required":[63],"adaptation":[67],"can":[68,78,114],"be":[69,79,115],"reduced":[70],"by":[71,82,88],"introducing":[72,138],"inductive":[73,139],"biases":[74,140],"into":[75],"DNNs,":[76],"learned":[80],"automatically":[81],"meta-learning":[83,99,178],"or":[84,150],"embedded":[85],"DNNs":[87],"designing":[89],"graph":[90],"networks":[92],"(GNNs).":[93],"Almost":[94],"all":[95],"previous":[96],"works":[97],"on":[98],"overlooked":[100],"prior-known":[102],"permutation":[103],"equivariance":[104],"(PE)":[105],"properties,":[106],"widely":[108],"exist":[109],"harnessed":[116],"reduce":[118],"hypothesis":[120],"space":[121],"DNN.":[124],"In":[125],"this":[126],"paper,":[127],"we":[128],"strive":[129],"answer":[131,163],"following":[133],"question:":[134],"way":[136],"more":[142,173],"effective":[143],"reducing":[145],"retraining,":[148],"GNNs":[149,171],"meta-learning?":[151],"We":[152],"take":[153],"sum-rate":[155],"maximization":[156],"problem":[158],"an":[160],"example":[161],"question.":[165],"Simulation":[166],"results":[167],"show":[168],"efficient":[174],"than":[175],"meta-learning,":[176],"cannot":[181],"scenarios":[185],"where":[186],"number":[188],"users":[190],"differs":[191],"scenario.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
