论文标题
迭代软输入软输出解码，有序可靠性位
Iterative Soft-Input Soft-Output Decoding with Ordered Reliability Bits GRAND
论文作者
论文摘要
猜测随机的添加噪声解码（GRAND）是一种通用解码算法，可用于执行最大似然解码。它试图通过生成一系列可能的误差向量来查找通道引入的错误，该序列可能是发生的可能性顺序，并将其应用于接收的向量。订购的可靠性位Grand（Orbgrand）集成了从通道接收的软信息，以完善误差向量序列。在这项工作中，对Orbgrand进行了修改以产生软输出，以使其用作迭代软输入（SISO）解码器。然后提出了特定于迭代大的基于迭代的基于迭代的解码的三种技术，以提高错误纠正性能并降低计算复杂性和延迟。使用OFEC代码作为案例研究，对提出的技术进行了评估，从而使基线SISO Orbgrand的绩效增长和惊人的复杂性降低了48 \％至85 \％。
Guessing Random Additive Noise Decoding (GRAND) is a universal decoding algorithm that can be used to perform maximum likelihood decoding. It attempts to find the errors introduced by the channel by generating a sequence of possible error vectors in order of likelihood of occurrence and applying them to the received vector. Ordered reliability bits GRAND (ORBGRAND) integrates soft information received from the channel to refine the error vector sequence. In this work, ORBGRAND is modified to produce a soft output, to enable its use as an iterative soft-input soft-output (SISO) decoder. Three techniques specific to iterative GRAND-based decoding are then proposed to improve the error-correction performance and decrease computational complexity and latency. Using the OFEC code as a case study, the proposed techniques are evaluated, yielding substantial performance gain and astounding complexity reduction of 48\% to 85\% with respect to the baseline SISO ORBGRAND.
