Sign Aided Adaptive Noisy Gradient Descent Bit-Flipping Algorithm for LDPC Codes
Keyue Deng, Yuxing Chen, Suwen Song, Zhongfeng Wang · 2022
The adjustment factor aided noisy gradient descent bit-flipping (ANGDBF) offers a significant improvement in error correction capability compared with other gradient descent bit-flipping (GDBF) variants. However, there still exists a considerable performance gap between the ANGDBF algorithm and the belief propagation (BP) algorithm. This paper presents a sign aided adaptive noisy GDBF (SANGDBF) algorithm, in which the mean of noise perturbation shows an adaptive property, facilitating the decoding process to escape from the local maxima more effectively. Moreover, the sign of the correlation term is fully utilized to improve the reliability of the inversion function. Simulation results show that the SANGDBF algorithm can achieve about 0.5 dB coding gain compared with the ANGDBF algorithm with limited hardware overhead.