We introduce a soft-detection variant of Guessing Random Additive Noise Decoding (GRAND) called Quantized GRAND (QGRAND) that can efficiently decode any moderate redundancy block-code of any length in an algorithm that is suitable for highly parallelized implementation in hardware. QGRAND can avail of any level of quantized soft information, is established to be almost capacity achieving, and is shown to provide near maximum likelihood decoding performance when provided with five or more bits of soft information per received bit.

翻译：我们引入了一个软检测变体,即 " 猜想随机添加噪声解码 " (GRAND),称为 " 量化GRAND(QGRAND) " (GRAND),该变体可以有效地解码任何适合硬件高度平行执行的算法中任何长度的中度冗余区码。 QGRAND可以利用任何水平的量化软信息,几乎可以实现能力,并显示在每收到一位提供五位或五位以上软信息时,几乎可以提供最大可能的解码性能。