用可训练神经权重改进低密度校验码解码，提升效率且不降性能。
Neural Window Decoder for SC-LDPC Codes
- 在传统窗口解码中引入可训练神经权重，优化更新过程。
- 跳过41%低重要性校验节点更新，性能不变且加速解码。
- 检测错误后激活互补权重，有效抑制误传问题，适合通信系统部署。
本文提出一种用于空间耦合低密度校验码（SC-LDPC）的神经窗口解码器（NWD）。NWD保留传统窗口解码流程，但引入可训练的神经权重。为训练这些权重，提出两种新策略：一是仅针对窗口内变量节点设计损失函数，减少网络复杂度，提升训练效率；二是采用带归一化损失项的主动学习技术，避免训练偏向特定区域。随后，基于训练结果提出非均匀调度方法，引入可训练阻尼因子以反映校验节点更新的重要性。通过跳过重要性较低的更新，可省去41%的校验节点更新，性能与传统窗口解码相当。最后，针对SC-LDPC码固有的误传问题，引入互补权重集，在前一窗口检测到错误时激活，实现自适应解码，有效缓解误传，无需修改码结构或解码器架构。
原文摘要 · Abstract (English)
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.
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