{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116222"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116222","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Curriculum learning for polar and PAC decoders","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Nadkarni, Viraj"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Viswanath, Pramod"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["Channel coding","Machine learning"],"languages":["en","eng"],"rights":["Copyright 2022 Viraj Nadkarni"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116222","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Viswanath, Pramod"]},{"key":"dc:creator","label":"Author","values":["Nadkarni, Viraj"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-19"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Channel coding","Machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Viraj Nadkarni"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116222"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Viraj Nadkarni, accepted the attached license on 2022-07-12 at 14:09.","The student, Viraj Nadkarni, submitted this Thesis for approval on 2022-07-12 at 14:14.","This Thesis was approved for publication on 2022-07-19 at 16:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18260 on 2022-11-15 at 18:20:47","Polar codes are widely used state-of-the-art codes for reliable communication that have recently been included in the 5th generation wireless standards (5G). Polar-Adjusted Convolutional (PAC) codes are a recent modification to Polar codes that provide better reliability even at shorter block lengths. Training efficient neural decoders for both kinds of codes proves challenging at longer block lengths or smaller model sizes. We show that the technique of Curriculum learning is useful in training such models. We also show that particular kinds of curricula work better than others in training the network and also explain reasons why this is the case using the structure of the encoding procedure in Polar codes. Various neural architectures, such as transformers, recurrent neural networks and convolutional neural networks, are tried. For a few codes, anomalous behaviour is observed in terms of which curriculum works the best based on what architecture is used."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Curriculum learning for polar and PAC decoders"]}]}],"canonical_facts":{"dc:contributor":["Viswanath, Pramod"],"dc:creator":["Nadkarni, Viraj"],"dc:date":["2022-08","2022-07-19"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Viraj Nadkarni, accepted the attached license on 2022-07-12 at 14:09.","The student, Viraj Nadkarni, submitted this Thesis for approval on 2022-07-12 at 14:14.","This Thesis was approved for publication on 2022-07-19 at 16:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18260 on 2022-11-15 at 18:20:47","Polar codes are widely used state-of-the-art codes for reliable communication that have recently been included in the 5th generation wireless standards (5G). Polar-Adjusted Convolutional (PAC) codes are a recent modification to Polar codes that provide better reliability even at shorter block lengths. Training efficient neural decoders for both kinds of codes proves challenging at longer block lengths or smaller model sizes. We show that the technique of Curriculum learning is useful in training such models. We also show that particular kinds of curricula work better than others in training the network and also explain reasons why this is the case using the structure of the encoding procedure in Polar codes. Various neural architectures, such as transformers, recurrent neural networks and convolutional neural networks, are tried. For a few codes, anomalous behaviour is observed in terms of which curriculum works the best based on what architecture is used."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116222"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Viraj Nadkarni"],"dc:subject":["Channel coding","Machine learning"],"dc:title":["Curriculum learning for polar and PAC decoders"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}