Learning to Decode Linear Codes Using Deep Learning
This work addresses decoding efficiency in communication systems, but it is incremental as it builds on existing belief propagation methods.
The authors tackled the problem of improving belief propagation decoding for linear codes by introducing a deep learning method that assigns trainable weights to Tanner graph edges, achieving demonstrated improvements for various high-density parity check codes.
A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to the edges of the Tanner graph. These edges are then trained using deep learning techniques. A well-known property of the belief propagation algorithm is the independence of the performance on the transmitted codeword. A crucial property of our new method is that our decoder preserved this property. Furthermore, this property allows us to learn only a single codeword instead of exponential number of code-words. Improvements over the belief propagation algorithm are demonstrated for various high density parity check codes.