论文标题
修剪神经信念传播解码器
Pruning Neural Belief Propagation Decoders
论文作者
论文摘要
我们考虑基于Nachmani等人最近引入的基于神经信念传播（BP）解码的短线性块代码的最大最大样品（ML）解码。尽管该方法显着超过常规的BP解码，但基本的平等检查基质可能仍会限制整体性能。在本文中，我们介绍了一种使用机器学习来定制（神经）BP解码的过度均等检查矩阵的方法。我们将Tanner图中的权重视为指示连接的校验节点（CNS）对解码和使用它们来修剪无关紧要的CNS的重要性。由于修剪并未在迭代上绑定，因此最终解码器在每次迭代中使用不同的奇偶校验检查矩阵。对于Reed-Muller和短密度均衡检查代码，我们在降低解码器的复杂性的同时，达到了0.27 dB和1.5 dB的性能。
We consider near maximum-likelihood (ML) decoding of short linear block codes based on neural belief propagation (BP) decoding recently introduced by Nachmani et al.. While this method significantly outperforms conventional BP decoding, the underlying parity-check matrix may still limit the overall performance. In this paper, we introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We consider the weights in the Tanner graph as an indication of the importance of the connected check nodes (CNs) to decoding and use them to prune unimportant CNs. As the pruning is not tied over iterations, the final decoder uses a different parity-check matrix in each iteration. For Reed-Muller and short low-density parity-check codes, we achieve performance within 0.27 dB and 1.5 dB of the ML performance while reducing the complexity of the decoder.
