On Deep Learning-Based Channel Decoding
arXiv:1701.07738
Abstract
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.
accepted for CISS 2017
Cited by in corpus (5)
- Deep Learning for Wireless Physical Layer: Opportunities and Challenges
- RNN Decoding of Linear Block Codes
- A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access
- Deep Learning-Based Decoding of Constrained Sequence Codes
- Deep Learning Assisted Sum-Product Detection Algorithm for Faster-than-Nyquist Signaling