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Scaling Deep Learning-based Decoding of Polar Codes via Partitioning
[article]
2017
arXiv
pre-print
The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network decoding (NND) is currently only feasible for very short block lengths. In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components. Thus, we partition the encoding graph into smaller
arXiv:1702.06901v1
fatcat:tgahpsg3izg3jalq6nm3rbvohy