Curriculum learning for polar and PAC decoders

Nadkarni, Viraj

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

Description

Title

Curriculum learning for polar and PAC decoders

Author(s)

Nadkarni, Viraj

Issue Date

2022-07-19

Director of Research (if dissertation) or Advisor (if thesis)

Viswanath, Pramod

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

Channel coding, Machine learning

Abstract

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.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Viraj Nadkarni

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