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 some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple classifier. We introduce the metric normalized validation error (NVE) in order to further investigate the potential and limitations of deep learning-based decoding with respect to performance and complexity.
The authors of REF studied the decoder design using an artificial neural network and showed that their decoder could approach maximum a posteriori (MAP) performance for short codes.
On deep learning-based channel decoding
2017 51st Annual Conference on Information Sciences and Systems (CISS)
2017 51st Annual Conference on Information Sciences and Systems (CISS)
Mathematics Computer Science