  High quality data is essential in deep learning to train a robust model.
While in other fields data is sparse and costly to collect, in error decoding
it is free to query and label thus allowing potential data exploitation.
Utilizing this fact and inspired by active learning, two novel methods are
introduced to improve Weighted Belief Propagation (WBP) decoding. These methods
incorporate machine-learning concepts with error decoding measures. For
BCH(63,36), (63,45) and (127,64) codes, with cycle-reduced parity-check
matrices, improvement of up to 0.4dB at the waterfall region, and of up to
1.5dB at the errorfloor region in FER, over the original WBP, is demonstrated
by smartly sampling the data, without increasing inference (decoding)
complexity. The proposed methods constitutes an example guidelines for model
enhancement by incorporation of domain knowledge from error-correcting field
into a deep learning model. These guidelines can be adapted to any other deep
learning based communication block.
