Year: 2022
Type: dissertation
Abstract: Polar codes are widely used state-of-the-art codes for reliable communication that have recently been included in the 5 th 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.The work explicated in this thesis would not have been possible without the guidance of my adviser, Prof. Pramod Viswanath and close collaborators
Author: Nadkarni, Viraj
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7220768008