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 loss 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：随机加性噪声译码（GRAND）是一种近期提出的译码范式，特别适用于短码长和高码率场景。在其变体中，有序可靠度比特GRAND（ORBGRAND）利用软信息以简单有效的方式调度查询，从而支持高效的硬件实现。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损失。为在保持硬件实现友好性的同时提升ORBGRAND性能，本文提出一种名为RS-ORBGRAND的ORBGRAND新变体，其核心思想是通过重排ORBGRAND的查询顺序，使期望查询数最小化。数值仿真表明，当BLER低至$10^{-6}$时，RS-ORBGRAND相比ORBGRAND及其现有变体实现显著增益，且与ML译码的差距仅为0.1dB。