{"id":"https://openalex.org/W4280508843","doi":"https://doi.org/10.1109/wcnc51071.2022.9771688","title":"Learning Precoding Policy: CNN or GNN?","display_name":"Learning Precoding Policy: CNN or GNN?","publication_year":2022,"publication_date":"2022-04-10","ids":{"openalex":"https://openalex.org/W4280508843","doi":"https://doi.org/10.1109/wcnc51071.2022.9771688"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc51071.2022.9771688","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc51071.2022.9771688","pdf_url":null,"source":{"id":"https://openalex.org/S4363607776","display_name":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","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":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","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/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":["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":3,"corresponding_author_ids":["https://openalex.org/A5041458795"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":3.5601,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.94196216,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1027","last_page":"1032"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.998199999332428,"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":0.998199999332428,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9894999861717224,"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/precoding","display_name":"Precoding","score":0.7830969095230103},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7291291356086731},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5407398343086243},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5181461572647095},{"id":"https://openalex.org/keywords/inductive-bias","display_name":"Inductive bias","score":0.46780040860176086},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4609234035015106},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.452968567609787},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4393921196460724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43151307106018066},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.42571690678596497},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41060787439346313},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.3862539827823639},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.3167639672756195},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28615522384643555},{"id":"https://openalex.org/keywords/mimo","display_name":"MIMO","score":0.1314234733581543},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08922797441482544},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07822966575622559}],"concepts":[{"id":"https://openalex.org/C160562895","wikidata":"https://www.wikidata.org/wiki/Q7239557","display_name":"Precoding","level":4,"score":0.7830969095230103},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7291291356086731},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5407398343086243},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5181461572647095},{"id":"https://openalex.org/C197352929","wikidata":"https://www.wikidata.org/wiki/Q1074074","display_name":"Inductive bias","level":4,"score":0.46780040860176086},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4609234035015106},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.452968567609787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4393921196460724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43151307106018066},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.42571690678596497},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41060787439346313},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.3862539827823639},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3167639672756195},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28615522384643555},{"id":"https://openalex.org/C207987634","wikidata":"https://www.wikidata.org/wiki/Q176862","display_name":"MIMO","level":3,"score":0.1314234733581543},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08922797441482544},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07822966575622559},{"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},{"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/wcnc51071.2022.9771688","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc51071.2022.9771688","pdf_url":null,"source":{"id":"https://openalex.org/S4363607776","display_name":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","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":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1970830346","https://openalex.org/W2103972037","https://openalex.org/W2147800946","https://openalex.org/W2164350703","https://openalex.org/W2550848904","https://openalex.org/W2616867685","https://openalex.org/W2805516822","https://openalex.org/W2908498346","https://openalex.org/W2986348246","https://openalex.org/W2986661129","https://openalex.org/W3008723477","https://openalex.org/W3035449691","https://openalex.org/W3040589146","https://openalex.org/W3086007150","https://openalex.org/W3096252532","https://openalex.org/W3099684761","https://openalex.org/W3103097962","https://openalex.org/W6751796012","https://openalex.org/W6767854582"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3104240813","https://openalex.org/W3209042722","https://openalex.org/W1505595614","https://openalex.org/W1587378402","https://openalex.org/W2281006184"],"abstract_inverted_index":{"Optimizing":[0],"precoding":[1,73,83,93,138],"with":[2,145],"deep":[3],"learning":[4,13,59,72],"enables":[5],"its":[6],"real-time":[7],"implementation.":[8],"In":[9,85],"addition":[10],"to":[11,29,43,51,56,104,136,156],"the":[12,37,52,58,82,106,113,116,128,132,137,158],"perfor-mance":[14],"such":[15],"as":[16],"sum":[17],"rate,":[18],"training":[19,62,149,154],"complexity":[20,155],"is":[21,134],"also":[22],"important":[23],"since":[24],"neural":[25,69],"networks":[26,70],"(NNs)":[27],"have":[28],"be":[30,44,49],"re-trained":[31],"in":[32],"time-varying":[33],"channels.":[34],"By":[35],"leveraging":[36],"prior-known":[38],"property":[39,98],"for":[40,71],"a":[41],"policy":[42,94],"learned,":[45],"inductive":[46,79,119,129],"biases":[47,80,120],"can":[48],"introduced":[50],"structure":[53],"of":[54,121,131,148],"NNs":[55],"balance":[57],"performance":[60],"and":[61,99,111,118,140,151],"com-plexity.":[63],"Most":[64],"existing":[65],"works":[66],"use":[67],"convolutional":[68],"policy,":[74,139],"without":[75],"considering":[76],"whether":[77],"their":[78],"match":[81],"task.":[84],"this":[86],"paper,":[87],"we":[88],"first":[89],"show":[90,112,126],"that":[91,127],"full-digital":[92],"exhibits":[95],"permutation":[96],"equivariance":[97],"introduce":[100],"graph":[101],"NN":[102],"(GNN)":[103],"learn":[105],"policy.":[107],"We":[108],"then":[109],"analyze":[110],"connections":[114],"between":[115],"structures":[117],"several":[122],"NNs.":[123,163],"Simulation":[124],"results":[125],"bias":[130],"GNN":[133],"well-matched":[135],"hence":[141],"achieves":[142],"higher":[143],"sum-rate":[144,160],"given":[146],"number":[147],"samples":[150],"needs":[152],"lower":[153],"achieve":[157],"same":[159],"than":[161],"other":[162]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}