论文标题
宏伟的错误模式生成
Constrained Error Pattern Generation for GRAND
论文作者
论文摘要
最大样品（ML）解码可用于获得误差校正代码的最佳性能。但是，搜索空间的大小以及解码的复杂性呈指数增长，使得用于长期代码不切实际。在本文中，我们提出了一种方法，以限制最近引入的ML解码方案的误差模式的搜索空间，称为猜测随机加性噪声解码（GRAND）。在这种方法中，将基于综合征的约束将搜索空间划分为不相交集的约束。通过采用从奇偶校验检查矩阵中提取的$ p $约束，查询的平均数量减少了$ 2^p $，而错误校正性能仍然完好无损。
Maximum-likelihood (ML) decoding can be used to obtain the optimal performance of error correction codes. However, the size of the search space and consequently the decoding complexity grows exponentially, making it impractical to be employed for long codes. In this paper, we propose an approach to constrain the search space for error patterns under a recently introduced near ML decoding scheme called guessing random additive noise decoding (GRAND). In this approach, the syndrome-based constraints which divide the search space into disjoint sets are progressively evaluated. By employing $p$ constraints extracted from the parity check matrix, the average number of queries reduces by a factor of $2^p$ while the error correction performance remains intact.
