论文标题
使用伪柔软的信息逐渐消失的频道
GRAND for Fading Channels using Pseudo-soft Information
论文作者
论文摘要
猜测随机的加性噪声解码（GRAND）是一种通用的最大样本解码器，它通过猜测等级排序的推定噪声序列并反转其效果，直到获得一个或多个有效的代码字。这项工作探讨了如何在褪色渠道中利用添加额噪声统计信息和渠道状态信息。我们建议在频道均衡之后利用彩色噪声统计信息作为伪 - 软信息来分类噪声序列，而不是在检测器中计算每位可靠性信息并将这些信息传递给解码器。我们研究了从线性零效力和最小平方误差均衡中提取的伪柔软信息的功效，当时将其馈送到硬件友好的软源（Orbgrand）。我们证明，提出的伪柔软的大计划概述了使用完整软信息的CA-Polar和BCH代码最先进的解码器的性能。与硬源相比，Pseudo-Soft Orbgrand以10^-3块误差速率引入了多达10dB SNR的收益。
Guessing random additive noise decoding (GRAND) is a universal maximum-likelihood decoder that recovers code-words by guessing rank-ordered putative noise sequences and inverting their effect until one or more valid code-words are obtained. This work explores how GRAND can leverage additive-noise statistics and channel-state information in fading channels. Instead of computing per-bit reliability information in detectors and passing this information to the decoder, we propose leveraging the colored noise statistics following channel equalization as pseudo-soft information for sorting noise sequences. We investigate the efficacy of pseudo-soft information extracted from linear zero-forcing and minimum mean square error equalization when fed to a hardware-friendly soft-GRAND (ORBGRAND). We demonstrate that the proposed pseudo-soft GRAND schemes approximate the performance of state-of-the-art decoders of CA-Polar and BCH codes that avail of complete soft information. Compared to hard-GRAND, pseudo-soft ORBGRAND introduces up to 10dB SNR gains for a target 10^-3 block-error rate.
