{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116261"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116261","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Channel decoding via machine learning","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":["Hebbar, Shibara Ashwin"],"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":"2026-07-22T22:24:56Z","subjects":["communication","machine learning","deep learning","reinforcement learning","channel coding","error correction coding"],"languages":["en","eng"],"rights":["Copyright 2022 Shibara Ashwin Hebbar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116261","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":["Hebbar, Shibara Ashwin"]}]},{"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":["communication","machine learning","deep learning","reinforcement learning","channel coding","error correction coding"]}]},{"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 Shibara Ashwin Hebbar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116261"]}]},{"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, Shibara Ashwin Hebbar, accepted the attached license on 2022-07-17 at 12:21.","The student, Shibara Ashwin Hebbar, submitted this Thesis for approval on 2022-07-17 at 12:28.","This Thesis was approved for publication on 2022-07-19 at 09:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18358 on 2022-11-15 at 18:21:23","Error-correcting codes (codes) are the backbone of the modern information age and were essential to the invention of groundbreaking technology such as WiFi, cellular, cable, and satellite modems. In this thesis, we focus on using machine learning techniques to design efficient and reliable data-driven decoders for state-of-the-art channel codes. In the first half of the thesis, we introduce a neural-augmented decoder for Turbo codes called TinyTurbo. TinyTurbo has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TinyTurbo exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TinyTurbo strongly generalizes across different rate, blocklengths, and trellises. In the second half of the thesis, we focus on designing data-driven decoders for the polar code family: Polar codes and PAC codes. We pose the decoding of PAC codes as a tree-search problem, and introduce PAC- DQN, a reinforcement learning based decoder. While PAC-DQN achieves a near-optimal reliability for short codes, it suffers from poor training sample complexity and is not scalable to larger codes. We then introduce CRISP, a GRU-powered neural decoder, which uses curriculum learning to achieve excellent reliability and scale to larger codes. We show that CRISP out-performs the successive-cancellation (SC) decoder and attains near-optimal reliability performance on the Polar(16, 32), Polar(22, 64) and PAC(16, 32) codes."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Channel decoding via machine learning"]}]}],"canonical_facts":{"dc:contributor":["Viswanath, Pramod"],"dc:creator":["Hebbar, Shibara Ashwin"],"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, Shibara Ashwin Hebbar, accepted the attached license on 2022-07-17 at 12:21.","The student, Shibara Ashwin Hebbar, submitted this Thesis for approval on 2022-07-17 at 12:28.","This Thesis was approved for publication on 2022-07-19 at 09:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18358 on 2022-11-15 at 18:21:23","Error-correcting codes (codes) are the backbone of the modern information age and were essential to the invention of groundbreaking technology such as WiFi, cellular, cable, and satellite modems. In this thesis, we focus on using machine learning techniques to design efficient and reliable data-driven decoders for state-of-the-art channel codes. In the first half of the thesis, we introduce a neural-augmented decoder for Turbo codes called TinyTurbo. TinyTurbo has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TinyTurbo exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TinyTurbo strongly generalizes across different rate, blocklengths, and trellises. In the second half of the thesis, we focus on designing data-driven decoders for the polar code family: Polar codes and PAC codes. We pose the decoding of PAC codes as a tree-search problem, and introduce PAC- DQN, a reinforcement learning based decoder. While PAC-DQN achieves a near-optimal reliability for short codes, it suffers from poor training sample complexity and is not scalable to larger codes. We then introduce CRISP, a GRU-powered neural decoder, which uses curriculum learning to achieve excellent reliability and scale to larger codes. We show that CRISP out-performs the successive-cancellation (SC) decoder and attains near-optimal reliability performance on the Polar(16, 32), Polar(22, 64) and PAC(16, 32) codes."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116261"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Shibara Ashwin Hebbar"],"dc:subject":["communication","machine learning","deep learning","reinforcement learning","channel coding","error correction coding"],"dc:title":["Channel decoding via machine learning"],"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":"2026-07-22T22:24:56Z"}