论文标题
符号级别的宏伟宏伟，用于平面褪色频道的高阶调制
Symbol-Level GRAND for High-Order Modulation over Flat Fading Channels
论文作者
论文摘要
猜测随机添加噪声解码（GRAND）是一种以噪声为中心的解码方法，它适用于非常可靠的低延迟通信，因为它支持生成短长度代码字的高速误差校正代码。大估计通过猜测在传输过程中改变它们的误差模式来传输代码字。猜测过程需要以越来越多的锤击重量顺序排列的误差模式的生成和测试。这种方法适合于添加白色高斯噪声通道上的二元传输。这封信考虑了通过扁平褪色渠道传输编码和调制数据的传输，并提出了Grand的变体，该变体利用了调制方案和褪色渠道的信息。在所提出的变体的核心中，称为符号级别的大grand，是一个分析表达式，它计算出误差模式发生的可能性，并确定测试误差模式的顺序。仿真结果表明，符号级别的大grand产生的估计值估计值速度要快于原始grand，而内存需求的成本却很小。
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.
