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Found 5 papers, 1 papers with code
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.
Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes
Next, we derive the soft-decision based version of our algorithm, called soft-subRPA, that not only improves upon the performance of subRPA but also enables a differentiable decoding algorithm.
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.
KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning
In this paper, we construct KO codes, a computationaly efficient family of deep-learning driven (encoder, decoder) pairs that outperform the state-of-the-art reliability performance on the standardized AWGN channel.
Reed-Muller Subcodes: Machine Learning-Aided Design of Efficient Soft Recursive Decoding
To lower the complexity of our decoding algorithm, referred to as subRPA in this paper, we investigate different ways for pruning the projections.
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