{"id":"https://openalex.org/W4315783205","doi":"https://doi.org/10.1109/gcwkshps56602.2022.10008601","title":"Graph Neural Networks for Channel Decoding","display_name":"Graph Neural Networks for Channel Decoding","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4315783205","doi":"https://doi.org/10.1109/gcwkshps56602.2022.10008601"},"language":"en","primary_location":{"id":"doi:10.1109/gcwkshps56602.2022.10008601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps56602.2022.10008601","pdf_url":null,"source":{"id":"https://openalex.org/S4363605011","display_name":"2022 IEEE Globecom Workshops (GC Wkshps)","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 Globecom Workshops (GC Wkshps)","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/A5018506109","display_name":"Sebastian Cammerer","orcid":"https://orcid.org/0000-0002-7614-5156"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sebastian Cammerer","raw_affiliation_strings":["NVIDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074728359","display_name":"Jakob Hoydis","orcid":"https://orcid.org/0000-0002-0438-967X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jakob Hoydis","raw_affiliation_strings":["NVIDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076265502","display_name":"Fay\u00e7al Ait Aoudia","orcid":"https://orcid.org/0000-0003-2723-6171"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Faycal Ait Aoudia","raw_affiliation_strings":["NVIDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004577729","display_name":"Alexander Keller","orcid":"https://orcid.org/0000-0001-5716-3634"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexander Keller","raw_affiliation_strings":["NVIDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"486","last_page":"491"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.9998000264167786,"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/T11321","display_name":"Error Correcting Code Techniques","score":0.9998000264167786,"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/T10796","display_name":"Cooperative Communication and Network Coding","score":0.9800000190734863,"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/T10207","display_name":"Advanced biosensing and bioanalysis techniques","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.8266878724098206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7305334210395813},{"id":"https://openalex.org/keywords/bch-code","display_name":"BCH code","score":0.6575193405151367},{"id":"https://openalex.org/keywords/low-density-parity-check-code","display_name":"Low-density parity-check code","score":0.6566902995109558},{"id":"https://openalex.org/keywords/berlekamp\u2013welch-algorithm","display_name":"Berlekamp\u2013Welch algorithm","score":0.6244601607322693},{"id":"https://openalex.org/keywords/sequential-decoding","display_name":"Sequential decoding","score":0.6111725568771362},{"id":"https://openalex.org/keywords/list-decoding","display_name":"List decoding","score":0.5649000406265259},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5115238428115845},{"id":"https://openalex.org/keywords/belief-propagation","display_name":"Belief propagation","score":0.5019540786743164},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35929059982299805},{"id":"https://openalex.org/keywords/concatenated-error-correction-code","display_name":"Concatenated error correction code","score":0.2739676237106323},{"id":"https://openalex.org/keywords/block-code","display_name":"Block code","score":0.24536198377609253}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8266878724098206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7305334210395813},{"id":"https://openalex.org/C42276685","wikidata":"https://www.wikidata.org/wiki/Q795705","display_name":"BCH code","level":3,"score":0.6575193405151367},{"id":"https://openalex.org/C67692717","wikidata":"https://www.wikidata.org/wiki/Q187444","display_name":"Low-density parity-check code","level":3,"score":0.6566902995109558},{"id":"https://openalex.org/C132727834","wikidata":"https://www.wikidata.org/wiki/Q4892328","display_name":"Berlekamp\u2013Welch algorithm","level":5,"score":0.6244601607322693},{"id":"https://openalex.org/C193969084","wikidata":"https://www.wikidata.org/wiki/Q7452500","display_name":"Sequential decoding","level":4,"score":0.6111725568771362},{"id":"https://openalex.org/C204397858","wikidata":"https://www.wikidata.org/wiki/Q4437907","display_name":"List decoding","level":5,"score":0.5649000406265259},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5115238428115845},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.5019540786743164},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35929059982299805},{"id":"https://openalex.org/C78944582","wikidata":"https://www.wikidata.org/wiki/Q5158264","display_name":"Concatenated error correction code","level":4,"score":0.2739676237106323},{"id":"https://openalex.org/C157125643","wikidata":"https://www.wikidata.org/wiki/Q884707","display_name":"Block code","level":3,"score":0.24536198377609253}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcwkshps56602.2022.10008601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps56602.2022.10008601","pdf_url":null,"source":{"id":"https://openalex.org/S4363605011","display_name":"2022 IEEE Globecom Workshops (GC Wkshps)","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 Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1536930200","https://openalex.org/W2118263817","https://openalex.org/W2126259959","https://openalex.org/W2128765501","https://openalex.org/W2133068391","https://openalex.org/W2137813581","https://openalex.org/W2139584447","https://openalex.org/W2150498905","https://openalex.org/W2156094048","https://openalex.org/W2179644698","https://openalex.org/W2341277784","https://openalex.org/W2528151179","https://openalex.org/W2584943905","https://openalex.org/W2734408173","https://openalex.org/W2883265831","https://openalex.org/W2901383498","https://openalex.org/W2963204888","https://openalex.org/W2963408536","https://openalex.org/W2964070430","https://openalex.org/W3036589489","https://openalex.org/W3112330636","https://openalex.org/W3128783847","https://openalex.org/W3184425224","https://openalex.org/W3188522200","https://openalex.org/W4221162285","https://openalex.org/W4226191400","https://openalex.org/W4283830681","https://openalex.org/W4297094427","https://openalex.org/W4385834365","https://openalex.org/W6631943919","https://openalex.org/W6728127629","https://openalex.org/W6736685754","https://openalex.org/W6753505025","https://openalex.org/W6773956970","https://openalex.org/W6810035324","https://openalex.org/W6810937164"],"related_works":["https://openalex.org/W1995527419","https://openalex.org/W2385322349","https://openalex.org/W4244153028","https://openalex.org/W4385859070","https://openalex.org/W4380030966","https://openalex.org/W2118148379","https://openalex.org/W2521769401","https://openalex.org/W2159021452","https://openalex.org/W2167340446","https://openalex.org/W1978153242"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,148],"propose":[4],"a":[5,18,39,44,50,150],"fully":[6,169],"differentiable":[7],"graph":[8,52],"neural":[9,40],"network":[10,41],"(GNN)-based":[11],"architecture":[12],"for":[13,22,143],"channel":[14,109],"decoding":[15,20,78,110,131,139],"and":[16,31,65,89,141],"showcase":[17],"competitive":[19,151],"performance":[21,152],"various":[23],"coding":[24],"schemes,":[25],"such":[26],"as":[27,111,113],"low-density":[28],"parity-check":[29],"(LDPC)":[30],"BCH":[32,122,161],"codes.":[33],"The":[34],"idea":[35],"is":[36,92],"to":[37,73,85,155],"let":[38],"(NN)":[42],"learn":[43],"generalized":[45],"message":[46,67],"passing":[47],"algorithm":[48],"over":[49],"given":[51],"that":[53],"represents":[54],"the":[55,80,90,96,120,160,163],"forward":[56],"error":[57],"correction":[58],"(FEC)":[59],"code":[60],"structure":[61],"by":[62,95,132],"replacing":[63],"node":[64],"edge":[66],"updates":[68],"with":[69,136,171],"trainable":[70],"functions.":[71],"Contrary":[72],"many":[74],"other":[75],"deep":[76,116],"learning-based":[77,117],"approaches,":[79],"proposed":[81,103],"solution":[82,125],"enjoys":[83],"scalability":[84],"arbitrary":[86],"block":[87],"lengths":[88],"training":[91],"not":[93],"limited":[94],"curse":[97],"of":[98],"dimensionality.":[99],"We":[100],"benchmark":[101],"our":[102,124],"decoder":[104,166],"against":[105,114],"state-of-the-art":[106],"in":[107],"conventional":[108,156],"well":[112],"recent":[115],"results.":[118],"For":[119,159],"(63,45)":[121],"code,":[123],"outperforms":[126],"weighted":[127],"belief":[128],"propagation":[129],"(BP)":[130],"approximately":[133],"0.4":[134],"dB":[135],"significantly":[137],"less":[138],"iterations":[140],"even":[142],"5G":[144],"NR":[145],"LDPC":[146],"codes,":[147,162],"observe":[149],"when":[153],"compared":[154],"BP":[157],"decoding.":[158],"resulting":[164],"GNN":[165],"can":[167],"be":[168],"parametrized":[170],"only":[172],"9640":[173],"weights.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}