论文标题
通过BP-RNN多样性和基于可靠性的后处理来解码简短的LDPC代码
Decoding Short LDPC Codes via BP-RNN Diversity and Reliability-Based Post-Processing
论文作者
论文摘要
本文基于信仰传播（BP）算法的复发性神经网络（RNN）模型，研究了短密度平价检查（LDPC）代码的解码器多样性体系结构。我们提出了一种新的方法，通过将BP-RNN解码器专门针对特定的错误类别，并具有吸收的固定支持，以实现瀑布区域的解码器多样性。我们进一步将方法与有序的统计解码（OSD）后处理步骤相结合，该步骤有效地利用了使用二进制交叉透射损失函数得出的位率率优化。我们表明，单个专业的BP-RNN解码器与OSD后处理步骤结合了比BP更好。此外，将OSD后处理与使用多个BP-RNN解码器所带来的多样性相结合，提供了一种有效的方法来弥合差距，以最大程度地解码。
This paper investigates decoder diversity architectures for short low-density parity-check (LDPC) codes, based on recurrent neural network (RNN) models of the belief-propagation (BP) algorithm. We propose a new approach to achieve decoder diversity in the waterfall region, by specializing BP-RNN decoders to specific classes of errors, with absorbing set support. We further combine our approach with an ordered statistics decoding (OSD) post-processing step, which effectively leverages the bit-error rate optimization deriving from the use of the binary cross-entropy loss function. We show that a single specialized BP-RNN decoder combines better than BP with the OSD post-processing step. Moreover, combining OSD post-processing with the diversity brought by the use of multiple BP-RNN decoders, provides an efficient way to bridge the gap to maximum likelihood decoding.
