This paper is concerned with a search-number-reduced guessing random additive noise decoding (GRAND) algorithm for linear block codes, called partially constrained GRAND (PC-GRAND). In contrast to the original GRAND, which guesses error patterns without constraints, the PC-GRAND guesses only those error patterns satisfying partial constraints of the codes. In particular, the PC-GRAND takes partial rows of the parity-check matrix as constraints for generating candidate error patterns and the remaining rows as checks for validating the candidates. The number of searches can be reduced when the serial list Viterbi algorithm (SLVA) is implemented for searching over a trellis specified by the partial parity-check matrix. This is confirmed by numerical results. Numerical simulations are also provided for comparison with other decoding algorithms.

翻译：本文研究一种降低搜索次数的线性分组码猜随机加性噪声译码（GRAND）算法，称为部分约束GRAND（PC-GRAND）。与原始GRAND无约束地猜测错误模式不同，PC-GRAND仅猜测那些满足码字部分约束条件的错误模式。具体而言，PC-GRAND以校验矩阵的部分行作为生成候选错误模式的约束条件，以剩余行作为验证候选模式的校验条件。当采用串行列表维特比算法（SLVA）在由部分校验矩阵指定的网格图上进行搜索时，可减少搜索次数。数值结果证实了该方法的有效性，并提供了与其他译码算法对比的仿真数据。