Guessing random additive noise decoding (GRAND) is a recently proposed decoding paradigm particularly suitable for codes with short length and high rate. Among its variants, ordered reliability bits GRAND (ORBGRAND) exploits soft information in a simple and effective fashion to schedule its queries, thereby allowing efficient hardware implementation. Compared with maximum likelihood (ML) decoding, however, ORBGRAND still exhibits noticeable performance gap in terms of block error rate (BLER). In order to improve the performance of ORBGRAND while still retaining its amenability to hardware implementation, a new variant of ORBGRAND termed RS-ORBGRAND is proposed, whose basic idea is to reshuffle the queries of ORBGRAND so that the expected number of queries is minimized. Numerical simulations show that RS-ORBGRAND leads to noticeable gains compared with ORBGRAND and its existing variants, and is only 0.1dB away from ML decoding, for BLER as low as $10^{-6}$.

翻译：猜测随机加性噪声译码（GRAND）是一种近期提出的译码范式，特别适用于短码长、高码率的编码方案。在其变体中，有序可靠度比特GRAND（ORBGRAND）以简单高效的方式利用软信息来调度查询，从而便于硬件实现。然而，与最大似然（ML）译码相比，ORBGRAND在误块率（BLER）方面仍存在显著性能差距。为提升ORBGRAND性能并保留其硬件实现友好性，提出了一种名为RS-ORBGRAND的新变体，其核心思想是重排ORBGRAND的查询顺序，以最小化期望查询次数。数值仿真表明：在BLER低至$10^{-6}$时，RS-ORBGRAND相较于ORBGRAND及其现有变体具有显著增益，且距ML译码仅差0.1dB。