A New Probabilistic Gradient Descent Bit Flipping Decoder for LDPC Codes
Hangxuan Cui, Jun Ting Lin, Suwen Song, Zhongfeng Wang · 2019
Probabilistic gradient descent bit-flipping (PGDBF) is the state-of-the-art hard-decision algorithm for decoding low-density parity-check (LDPC) codes on binary symmetric channel (BSC). However, there still exists a considerable performance gap between the PGDBF algorithm and soft-decision algorithms, especially in the error-floor region. To bridge this performance gap, a tabu-list aided PGDBF (T-PGDBF) algorithm is proposed in this paper. In the T-PGDBF algorithm, a tabu-list is employed to help the decoding escape from trapping sets, which is the main cause of the error-floor phenomenon. The bits which are flipped in the current iteration will be added to the tabu-list to prevent them being flipped in the next iteration. Simulation results show that the T-PGDBF algorithm offers a significant performance gain when compared to the PGDBF algorithm, which can reach that of soft-decision algorithms. We also present the hardware architecture to implement the T-PGDBF algorithm. Synthesis results show that the improved performance offered by the T-PGDBF algorithm can be obtained with a small hardware overhead.