Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach
Abstract
We present a novel framework for applying deep neural networks (DNN) to soft decoding of linear codes at arbitrary block lengths. Unlike other approaches, our framework allows unconstrained DNN design, enabling the free application of powerful designs that were developed in other contexts. Our method is robust to overfitting that inhibits many competing methods, which follows from the exponentially large number of codewords required for their training. We achieve this by transforming the channel output before feeding it to the network, extracting only the syndrome of the hard decisions and the channel output reliabilities. We prove analytically that this approach does not involve any intrinsic performance penalty, and guarantees the generalization of performance obtained during training. Our best results are obtained using a recurrent neural network (RNN) architecture combined with simple preprocessing by permutation. We provide simulation results that demonstrate performance that sometimes approaches that of the ordered statistics decoding (OSD) algorithm.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2018
- DOI:
- 10.48550/arXiv.1802.04741
- arXiv:
- arXiv:1802.04741
- Bibcode:
- 2018arXiv180204741B
- Keywords:
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- Computer Science - Information Theory;
- Computer Science - Machine Learning;
- Computer Science - Neural and Evolutionary Computing
- E-Print:
- The first two authors contributed equally to this work. A shortened version was submitted to the ISIT 2018 conference