Guessing Random Additive Noise Decoding (GRAND) is a recently proposed decoding method searching for the error pattern applied to the transmitted codeword. Ordered reliability bit GRAND (ORBGRAND) uses soft channel information to reorder entries of error patterns, generating them according to a fixed schedule, i.e. their logistic weight. In this paper, we show that every good ORBGRAND scheduling should follow an universal partial order, and we present an algorithm to generate the logistic weight order accordingly. We then propose an improved error pattern schedule that can improve the performance of ORBGRAND of 0.5dB at a block error rate (BLER) of $10^{-5}$, with increasing gains as the BLER decreases. This schedule can be closely approximated with a low-complexity generation algorithm that is shown to incur no BLER degradation.
翻译:猜测随机添加噪声解码( GRAND) 是最近提议的一种解码方法, 以查找对传输的编码词应用的错误模式。 命令的可靠性比 GRAND (ORBGRAND) 使用软频道信息来重新排序错误模式的条目, 并按照固定的时间表, 即其后勤重量来生成这些条目。 在本文中, 我们显示每个好的ORBGRAND 列表都应该遵循一个普遍的局部顺序, 我们提出一个算法来据此生成后勤权重顺序。 然后我们提出一个改进的错误模式表, 改进OBGRAND 0. 0. 5dB 的性能, 以区块误率( lebR) 10 5 $ ( $) 为单位差错率( 10 ⁇ 5 } $ ), 并随着 BLER 的递减而增加收益。 这个时间表可以与低兼容度生成的算法相近, 显示不会导致 BLER 退化 。