2026-09-21T03:24:01Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/116222 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Viswanath, Pramod 2022-08 application/pdf en text 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 Curriculum learning for polar and PAC decoders Nadkarni, Viraj 2022-07-19 Channel coding Machine learning 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. Thesis eng https://hdl.handle.net/2142/116222 Copyright 2022 Viraj Nadkarni M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng