Low-Complexity Error Correction Algorithm for Cyclic Redundancy Check Codes

Recently, it has been shown that the Guessing Random Additive Noise Decoding (GRAND) algorithm is promising for decoding cyclic redundancy check (CRC) codes. However, the decoder has a daunting complexity in order to achieve a satisfactory performance, which is a burden for practical implementation. In order to address the problem, we propose a low-complexity algorithm, called the Hybrid Metric GRAND (HM-GRAND) algorithm, for decoding CRC codes based on the GRAND algorithm. The proposed algorithm uses an algebraic decoder to limit the number of candidate error patterns in the GRAND algorithm, which can significantly reduce the decoding complexity. Moreover, we design an effective adaptive quantization scheme for the HM-GRAND algorithm. Simulation results suggest that the proposed HM-GRAND algorithm has comparable performance with the GRAND algorithm. In addition, the performance loss caused by the designed quantization scheme is negligible.