On deep learning-based channel decoding

  title={On deep learning-based channel decoding},
  author={Tobias Gruber and Sebastian Cammerer and Jakob Hoydis and Stephan ten Brink},
  journal={2017 51st Annual Conference on Information Sciences and Systems (CISS)},
We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide… CONTINUE READING
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