Curriculum learning for polar and PAC decoders

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Nadkarni, Viraj

Contributors dc:contributor

• Viswanath, Pramod

• Channel coding
• Machine learning

Statement dc:rights

• Copyright 2022 Viraj Nadkarni

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/116222

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F116222