Low-density parity-check (LDPC) codes have been successfully commercialized
in communication systems due to their strong error correction ability and
simple decoding process. However, the error-floor phenomenon of LDPC codes, in
which the error rate stops decreasing rapidly at a certain level, poses
challenges in achieving extremely low error rates and the application of LDPC
codes in scenarios demanding ultra high reliability. In this work, we propose
training methods to optimize neural min-sum (NMS) decoders that are robust to
the error-floor. Firstly, by leveraging the boosting learning technique of
ensemble networks, we divide the decoding network into two networks and train
the post network to be specialized for uncorrected codewords that failed in the
first network. Secondly, to address the vanishing gradient issue in training,
we introduce a block-wise training schedule that locally trains a block of
weights while retraining the preceding block. Lastly, we show that assigning
different weights to unsatisfied check nodes effectively lowers the error-floor
with a minimal number of weights. By applying these training methods to
standard LDPC codes, we achieve the best error-floor performance compared to
other decoding methods. The proposed NMS decoder, optimized solely through
novel training methods without additional modules, can be implemented into
current LDPC decoders without incurring extra hardware costs. The source code
is available at this https URL

通过训练方法优化神经最小和（NMS）解码器，有效降低 LDPC 码的误差底线现象，并获得最佳的误差底线性能。