论文标题
使用Grand柔软的最大似然解码
Soft Maximum Likelihood Decoding using GRAND
论文作者
论文摘要
已知对正向误差校正代码的最大似然（ML）解码是最佳准确的，但在实践中不使用，因为它证明这太具有挑战性了，无法有效实施。在这里，我们介绍了一个名为Sgrand的ML解码器，该解码器是先前描述的硬检测ML解码器的开发，该解码器称为Grand，它充分利用软检测信息，适合与任何任意高速，短长度的块代码一起使用。我们评估Sgrand在CRC辅助极性（CA极）代码上的性能，该代码将用于5G NR中的所有控制通道通信，将其精度与CRC辅助连续的取消列表解码（CA-SCL）（CA-SCL）（一种最先进的软性化解码器特定于CA Polar代码）进行了比较。
Maximum Likelihood (ML) decoding of forward error correction codes is known to be optimally accurate, but is not used in practice as it proves too challenging to efficiently implement. Here we introduce a ML decoder called SGRAND, which is a development of a previously described hard detection ML decoder called GRAND, that fully avails of soft detection information and is suitable for use with any arbitrary high-rate, short-length block code. We assess SGRAND's performance on CRC-aided Polar (CA-Polar) codes, which will be used for all control channel communication in 5G NR, comparing its accuracy with CRC-Aided Successive Cancellation List decoding (CA-SCL), a state-of-the-art soft-information decoder specific to CA-Polar codes.
