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
Language
eng
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.
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