Convolutional Polar Codes on Channels with Memory using Tensor Networks

Conference 2019 Benjamin Bourassa,Maxime Tremblay,David Poulin

Arikan’s recursive code construction is designed to polarize a collection of memoryless channels into a set of good and a set of bad channels, and it can be efficiently decoded using successive cancellation [1]. It was recently shown that the same construction also polarizes channels with memory [2], and a generalization of successive cancellation decoder was proposed with a complexity that scales like the third power of the channel’s memory size [3]... (read more)

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