  It is known belief propagation decoding variants of LDPC codes can be
unrolled easily as neural networks after assigning differed weights to message
passing edges flexibly. In this paper we focus on how to determine these
weights, in the form of trainable paramters, within a framework of deep
learning. Firstly, a new method is proposed to generate high-quality training
data via exploiting an approximation to the targeted mixture density. Then the
strong positive correlation between training loss and decoding metrics is fully
exposed after tracing the training evolution curves. Lastly, for the purpose of
facilitating training convergence and reducing decoding complexity, we
highlight the necessity of slashing the number of trainable parameters while
emphasizing the locations of these survived ones, which is justified in the
extensive simulation.
