{"id":"https://openalex.org/W7127634174","doi":"https://doi.org/10.1109/ccnc65079.2026.11366411","title":"KAN-AE with Non-Linearity Score and Symbolic Regression for Energy-Efficient Channel Coding","display_name":"KAN-AE with Non-Linearity Score and Symbolic Regression for Energy-Efficient Channel Coding","publication_year":2026,"publication_date":"2026-01-09","ids":{"openalex":"https://openalex.org/W7127634174","doi":"https://doi.org/10.1109/ccnc65079.2026.11366411"},"language":null,"primary_location":{"id":"doi:10.1109/ccnc65079.2026.11366411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc65079.2026.11366411","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd Consumer Communications &amp;amp; Networking Conference (CCNC)","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/A5114388374","display_name":"Anthony Joseph Perre","orcid":null},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anthony Joseph Perre","raw_affiliation_strings":["University of South Carolina,Department of Electrical Engineering,Columbia,SC,USA"],"affiliations":[{"raw_affiliation_string":"University of South Carolina,Department of Electrical Engineering,Columbia,SC,USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108988557","display_name":"Parker Huggins","orcid":null},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Parker Huggins","raw_affiliation_strings":["University of South Carolina,Department of Electrical Engineering,Columbia,SC,USA"],"affiliations":[{"raw_affiliation_string":"University of South Carolina,Department of Electrical Engineering,Columbia,SC,USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112321991","display_name":"Alphan Sahin","orcid":null},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alphan \u015eahin","raw_affiliation_strings":["University of South Carolina,Department of Electrical Engineering,Columbia,SC,USA"],"affiliations":[{"raw_affiliation_string":"University of South Carolina,Department of Electrical Engineering,Columbia,SC,USA","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114388374"],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27969534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.9052000045776367,"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":0.9052000045776367,"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/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.00800000037997961,"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"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.007600000128149986,"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/autoencoder","display_name":"Autoencoder","score":0.6258999705314636},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5317999720573425},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5049999952316284},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.492900013923645},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4839000105857849},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.46779999136924744},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.4327999949455261},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4311999976634979}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6409000158309937},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6258999705314636},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5317999720573425},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5049999952316284},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.492900013923645},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4839000105857849},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46309998631477356},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45249998569488525},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35519999265670776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31690001487731934},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3001999855041504},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C65620979","wikidata":"https://www.wikidata.org/wiki/Q7661176","display_name":"Symbolic data analysis","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccnc65079.2026.11366411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc65079.2026.11366411","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd Consumer Communications &amp;amp; Networking Conference (CCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9050014615058899,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2734408173","https://openalex.org/W2807731816","https://openalex.org/W2888848736","https://openalex.org/W2963836746","https://openalex.org/W2964198392","https://openalex.org/W2966038277","https://openalex.org/W3088492475","https://openalex.org/W4413179843"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,20,42,63],"investigate":[4],"Kolmogorov-Arnold":[5],"network-based":[6],"autoencoders":[7],"(KAN-AEs)":[8],"with":[9,77,109],"symbolic":[10,24],"regression":[11],"(SR)":[12],"for":[13,126],"energy-efficient":[14,127],"channel":[15,130],"coding.":[16,131],"By":[17],"using":[18,88],"SR,":[19],"convert":[21],"KAN-AEs":[22,66,121],"into":[23],"expressions,":[25],"which":[26],"enables":[27],"low-complexity":[28],"implementation":[29,87],"and":[30,93],"improved":[31],"energy":[32,73,82,115],"efficiency":[33,74,83],"at":[34],"the":[35,40,50,81,89,106,117],"radios.":[36],"To":[37],"further":[38],"enhance":[39],"efficiency,":[41],"introduce":[43],"a":[44,85,97,123],"new":[45],"non-linearity":[46,91],"score":[47,80],"term":[48],"in":[49],"SR":[51,110],"process":[52],"to":[53,96],"help":[54],"select":[55],"lower-complexity":[56],"equations":[57],"when":[58,75],"possible.":[59],"Through":[60],"numerical":[61],"simulations,":[62],"demonstrate":[64],"that":[65,105,120],"achieve":[67],"competitive":[68],"BLER":[69],"performance":[70],"while":[71],"improving":[72],"paired":[76,108],"SR.":[78],"We":[79],"of":[84],"KAN-AE":[86,107],"proposed":[90],"metric":[92],"compare":[94],"it":[95],"multi-layer":[98],"perceptron-based":[99],"autoencoder":[100],"(MLP-AE).":[101],"Our":[102],"experiment":[103],"shows":[104],"uses":[111],"1.38":[112],"times":[113],"less":[114],"than":[116],"MLP-AE,":[118],"supporting":[119],"are":[122],"promising":[124],"choice":[125],"deep":[128],"learning-based":[129]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-02-06T00:00:00"}
