Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding technique for short-length and high-rate channel codes. GRAND tries to guess the channel noise by generating test error patterns (TEPs), and the sequence of the TEPs is the main difference between different GRAND variants. In this work, we extend the application of GRAND to multipath frequency non-selective Rayleigh fading communication channels, and we refer to this GRAND variant as Fading-GRAND. The proposed Fading-GRAND adapts its TEP generation to the fading conditions of the underlying communication channel, outperforming traditional channel code decoders in scenarios with $L$ spatial diversity branches as well as scenarios with no diversity. Numerical simulation results show that the Fading-GRAND outperforms the traditional Berlekamp-Massey (B-M) decoder for decoding BCH code $(127,106)$ and BCH code $(127,113)$ by $\mathbf{0.5\sim6.5}$ dB at a target FER of $10^{-7}$. Similarly, Fading-GRAND outperforms GRANDAB, the hard-input variation of GRAND, by $0.2\sim8$ dB at a target FER of $10^{-7}$ with CRC $(128,104)$ code and RLC $(128,104)$. Furthermore the average complexity of Fading-GRAND, at $\frac{E_b}{N_0}$ corresponding to target FER of $10^{-7}$, is $\frac{1}{2}\times\sim \frac{1}{46}\times$ the complexity of GRANDAB.
翻译:GRAND 试图通过产生测试错误模式(TEPs)来猜测频道的噪音,而TEP的顺序是不同GRAND变体之间的主要差异。在这项工作中,我们将GRAND的应用扩大到多频非选择性的雷利淡化通信频道,我们将GRAND变种称为Fading-GRAND。拟议的FAND 将其TEP 生成适应基础通信频道的淡化条件,在使用美元空间多样性分支和没有多样性的假想中,比传统频道代码解码方格(TEPs ) 。我们把GRAND 应用到多频非选择性雷利淡化的通信渠道,我们把GRAND 应用到GRAND 代码(127,106美元) 和B代码(127,113美元) 调整到基通信频道的淡化条件,在使用美元空间多样性的假设情景中比传统的频道代码(0.50,6.5美元) 传统频道解码。