标题： 自适应学习的置信度传播用于解码纠错码 显示英文标题

标题： Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes

摘要： 加权信念传播（WBP）用于线性分组码的解码。在WBP中，代码的Tanner图根据信念传播解码器的迭代进行展开。然后，将权重分配给所得递归网络的边，并使用训练数据集进行离线优化。本文的主要贡献是一种自适应WBP，其中解码器的权重针对每个接收字确定。研究了该解码器的两种变体。在并行WBP解码器中，权重取值于一个离散集合。多个WBP解码器并行运行以实时搜索最佳权重序列。在两阶段解码器中，使用一个小的神经网络来动态确定每个接收字的WBP解码器的权重。所提出的自适应解码器在两个应用中相对于静态对应物表现出显著改进。在第一个应用中，Bose--Chaudhuri--Hocquenghem、极化和准循环低密度奇偶校验（QC-LDPC）码在加性高斯白噪声信道上使用。结果表明，在大约相同的解码复杂度下，自适应WBP的比特错误率（BERs）比静态WBP的BERs低一个数量级，具体取决于码、其速率和信噪比。第二个应用是一个为长距离非线性光纤维信道设计的级联码，其中内码是QC-LDPC码，外码是空间耦合LDPC码。在这种情况下，内码使用自适应WBP进行解码，而外码使用滑动窗口解码器和静态信念传播进行解码。结果表明，与神经归一化最小和解码器相比，自适应WBP提供了0.8 dB的编码增益，计算复杂度和解码延迟大致相同。 显示英文摘要

摘要： Weighted belief propagation (WBP) for the decoding of linear block codes is considered. In WBP, the Tanner graph of the code is unrolled with respect to the iterations of the belief propagation decoder. Then, weights are assigned to the edges of the resulting recurrent network and optimized offline using a training dataset. The main contribution of this paper is an adaptive WBP where the weights of the decoder are determined for each received word. Two variants of this decoder are investigated. In the parallel WBP decoders, the weights take values in a discrete set. A number of WBP decoders are run in parallel to search for the best sequence- of weights in real time. In the two-stage decoder, a small neural network is used to dynamically determine the weights of the WBP decoder for each received word. The proposed adaptive decoders demonstrate significant improvements over the static counterparts in two applications. In the first application, Bose--Chaudhuri--Hocquenghem, polar and quasi-cyclic low-density parity-check (QC-LDPC) codes are used over an additive white Gaussian noise channel. The results indicate that the adaptive WBP achieves bit error rates (BERs) up to an order of magnitude less than the BERs of the static WBP at about the same decoding complexity, depending on the code, its rate, and the signal-to-noise ratio. The second application is a concatenated code designed for a long-haul nonlinear optical fiber channel where the inner code is a QC-LDPC code and the outer code is a spatially coupled LDPC code. In this case, the inner code is decoded using an adaptive WBP, while the outer code is decoded using the sliding window decoder and static belief propagation. The results show that the adaptive WBP provides a coding gain of 0.8 dB compared to the neural normalized min-sum decoder, with about the same computational complexity and decoding latency.