Neural Offset Min-Sum Decoding

Understand

Recently, it was shown that if multiplicative weights are assigned to the edges of a Tanner graph used in belief propagation decoding, it is possible to use deep learning techniques to find values for the weights which improve the error-correction performance of the decoder.

• Unfortunately, this approach requires many multiplications, which are generally expensive operations.
• In this paper, we suggest a more hardware-friendly approach in which offset min-sum decoding is augmented with learnable offset parameters.
• Our method uses no multiplications and has a parameter count less than half that of the multiplicative algorithm.