Guessing random additive noise decoding (GRAND) is a noise-centric decoding method, which is suitable for ultra-reliable low-latency communications, as it supports high-rate error correction codes that generate short-length codewords. GRAND estimates transmitted codewords by guessing the error patterns that altered them during transmission. The guessing process requires the generation and testing of error patterns that are arranged in increasing order of Hamming weight. This approach is fitting for binary transmission over additive white Gaussian noise channels. This letter considers transmission of coded and modulated data over flat fading channels and proposes a variant of GRAND, which leverages information on the modulation scheme and the fading channel. In the core of the proposed variant, referred to as symbol-level GRAND, is an analytical expression that computes the probability of occurrence of an error pattern and determines the order with which error patterns are tested. Simulation results demonstrate that symbol-level GRAND produces estimates of the transmitted codewords notably faster than the original GRAND at the cost of a small increase in memory requirements.
翻译:随机猜测添加噪音解码(GRAND)是一种以噪音为中心的解码方法,适合超可信任的低纬度通信,因为它支持高率错误校正代码,产生短长的编码。GRAND估计通过猜测在传输过程中改变这些编码的错误模式,传送了编码词。 猜测过程要求生成和测试按照增加含汞重量的顺序排列的错误模式。 这个方法适合添加白高斯噪音频道的二进制传输。 这封信考虑在平坦淡化的频道上传输编码和调制数据,并提出了一个GRAND变量,它利用关于调制办法和淡化通道的信息。 在拟议变式的核心中,称为符号级GRAND,是一种分析表达,它计算出错误模式发生概率并确定测试错误模式的顺序。 模拟结果显示,符号级GRAND生成传输的编码的估计数明显快过原GRAND,其成本是少量的记忆要求。