论文标题
基于综合征的神经网络解码器的迭代误差分解
Iterative Error Decimation for Syndrome-Based Neural Network Decoders
论文作者
论文摘要
在这封信中，我们引入了一个新的基于综合征的解码器，其中深神经网络（DNN）估计了收到向量的可靠性和综合征的误差模式。所提出的算法通过迭代选择最自信的位置为错误模式的错误位来起作用，从而更新选择错误模式的新位置时收到的向量。 （63,45）和（63,36）BCH代码的仿真结果表明，所提出的方法的表现优于现有的神经网络解码器。此外，新的解码器具有灵活性，因为它可以在没有再培训的情况下将其应用于任何现有综合症的DNN解码器的顶部。
In this letter, we introduce a new syndrome-based decoder where a deep neural network (DNN) estimates the error pattern from the reliability and syndrome of the received vector. The proposed algorithm works by iteratively selecting the most confident positions to be the error bits of the error pattern, updating the vector received when a new position of the error pattern is selected. Simulation results for the (63,45) and (63,36) BCH codes show that the proposed approach outperforms existing neural network decoders. In addition, the new decoder is flexible in that it can be applied on top of any existing syndrome-based DNN decoder without retraining.
