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.

翻译：确保极高的可靠性对于6G网络中的信道编码至关重要。6G网络内下一代超可靠低时延通信（xURLLC）场景要求误帧率低于10⁻⁹。然而，作为5G新空口（NR）标准的低密度奇偶校验（LDPC）码面临称为"错误平层"现象的挑战，阻碍了达到如此低误码率的目标。为解决这一问题，我们提出一种创新方案：增强型神经最小和（NMS）解码器。该解码器运行机制与传统NMS解码器完全相同，但通过以下新型训练方法进行训练：i）利用未校正向量进行增强学习，ii）采用分块训练策略以解决梯度消失问题，iii）通过动态权重共享最小化可训练参数量，iv）运用迁移学习减少所需样本数量，v）采用数据增强加速采样过程。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码在不产生严重错误平层的情况下满足了6G xURLLC的要求。此外，增强型NMS解码器在权重训练完成后无需附加模块即可执行解码操作，具有立即投入实际应用的高度可行性。