In this paper, we propose a neural window decoder (NWD) for spatially coupled low-density parity-check (SC-LDPC) codes. The proposed NWD retains the conventional window decoder (WD) process but incorporates trainable neural weights. To train the weights of NWD, we introduce two novel training strategies. First, we restrict the loss function to target variable nodes (VNs) of the window, which prunes the neural network and accordingly enhances training efficiency. Second, we employ the active learning technique with a normalized loss term to prevent the training process from biasing toward specific training regions. Next, we develop a systematic method to derive non-uniform schedules for the NWD based on the training results. We introduce trainable damping factors that reflect the relative importance of check node (CN) updates. By skipping updates with less importance, we can omit $\mathbf{41\%}$ of CN updates without performance degradation compared to the conventional WD. Lastly, we address the error propagation problem inherent in SC-LDPC codes by deploying a complementary weight set, which is activated when an error is detected in the previous window. This adaptive decoding strategy effectively mitigates error propagation without requiring modifications to the code and decoder structures.

翻译：本文提出了一种用于空间耦合低密度奇偶校验（SC-LDPC）码的神经窗口译码器（NWD）。所提出的NWD保留了传统窗口译码器（WD）的处理流程，但引入了可训练的神经权重。为了训练NWD的权重，我们引入了两种新颖的训练策略。首先，我们将损失函数限制在窗口的目标变量节点（VNs）上，这修剪了神经网络并相应提高了训练效率。其次，我们采用主动学习技术结合归一化损失项，以防止训练过程偏向特定的训练区域。接着，我们基于训练结果开发了一种系统性的方法，为NWD推导非均匀调度方案。我们引入了可训练的阻尼因子，以反映校验节点（CN）更新的相对重要性。通过跳过重要性较低的更新，与传统WD相比，我们可以省略$\mathbf{41\%}$的CN更新而不会造成性能下降。最后，我们通过部署一个互补权重集来解决SC-LDPC码固有的错误传播问题，该权重集在检测到前一个窗口存在错误时被激活。这种自适应译码策略有效缓解了错误传播，且无需修改码和译码器结构。