Machine Learning Scaled Belief Propagation for Short Codes

The problem of finding good error correcting codes for short block lenghts and its corresponding decoders is an open research topic. A frequently applied soft decoder is the Belief Propagation (BP) decoder, however with degraded performance in case of short loops in the Tanner graph. This is especially problematic for short length codes as loops of small length are more likely to occur. In this paper, we propose the Machine Learning Scaled Belief Propagation (MLS-BP) to mitigate the performance loss of BP decoding for short length codes by introducing a learned scaling factor for the receive signals. The key point of this approach is the fact that the implementation of the BP decoder is not changed and the simple scaling leads to performance results comparable to other proposed BP improvements.