Ensuring extremely high reliability in channel coding is essential for 6G networks. The next-generation of ultra-reliable and low-latency communications (xURLLC) scenario within 6G networks requires 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 a 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. The source code is available at https://github.com/ghy1228/LDPC_Error_Floor.

翻译：确保信道编码的极高可靠性对于6G网络至关重要。6G网络中的下一代超可靠低时延通信（xURLLC）场景要求误帧率（FER）低于$10^{-9}$。然而，作为5G新空口（NR）标准的低密度奇偶校验（LDPC）码面临一个称为“错误平层”现象的挑战，阻碍了实现如此低误码率的目标。为解决这一问题，我们提出了一种创新方案：增强型神经最小和（NMS）解码器。该解码器的操作方式与传统NMS解码器完全相同，但通过新颖的训练方法进行训练，包括：i）利用未校正向量进行增强学习，ii）采用分块训练策略以应对梯度消失问题，iii）动态权重共享以最小化可训练参数数量，iv）迁移学习以减少所需样本数量，以及v）数据增强以加速采样过程。借助这些训练策略，增强型NMS解码器在降低错误平层方面实现了最先进的性能，同时具备优异的瀑布区性能。值得注意的是，我们使5G LDPC码满足了6G xURLLC的要求，且未出现严重的错误平层现象。此外，增强型NMS解码器在权重训练完成后，无需额外模块即可执行解码操作，这使其具备高度实用性，可立即投入应用。源代码发布于https://github.com/ghy1228/LDPC_Error_Floor。