Ensuring extremely high reliability is essential for channel coding in 6G
networks. The next-generation of ultra-reliable and low-latency communications
(xURLLC) scenario within 6G networks requires a frame error rate (FER) below
10-9. However, low-density parity-check (LDPC) codes, the standard in 5G new
radio (NR), encounter a challenge known as the error floor phenomenon, which
hinders to achieve such low rates. To tackle this problem, we introduce an
innovative solution: boosted neural min-sum (NMS) decoder. This decoder
operates identically to conventional NMS decoders, but is trained by novel
training methods including: i) boosting learning with uncorrected vectors, ii)
block-wise training schedule to address the vanishing gradient issue, iii)
dynamic weight sharing to minimize the number of trainable weights, iv)
transfer learning to reduce the required sample count, and v) data augmentation
to expedite the sampling process. Leveraging these training strategies, the
boosted NMS decoder achieves the state-of-the art performance in reducing the
error floor as well as superior waterfall performance. Remarkably, we fulfill
the 6G xURLLC requirement for 5G LDPC codes without the severe error floor.
Additionally, the boosted NMS decoder, once its weights are trained, can
perform decoding without additional modules, making it highly practical for
immediate application.

在 6G 网络中，确保极高的可靠性对于信道编码至关重要。我们提出一种创新的解决方案，即增强型神经最小和（NMS）解码器，通过采用新颖的训练方法，在 5G 低密度奇偶校验（LDPC）码中实现了优越的性能，并满足了 6G 低误码要求，同时具备高度的实际应用性。