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ProductAE: Toward Deep Learning Driven Error-Correction Codes of Large Dimensions
In this paper, we propose Product Autoencoder (ProductAE) -- a computationally-efficient family of deep learning driven (encoder, decoder) pairs -- aimed at enabling the training of relatively large codes (both encoder and decoder) with a manageable training complexity.
List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels
The listAE is a general framework and can be used with any AE architecture.
ProductAE: Towards Training Larger Channel Codes based on Neural Product Codes
Due the dimensionality challenge in channel coding, it is prohibitively complex to design and train relatively large neural channel codes via deep learning techniques.
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