Neural Adaptive Offest Min-Sum Algorithm for LDPC Decoding

With the rapid development of deep learning and its applications in physical layer communication, deep learning-based channel coding has gradually become a research hotspot. In this paper, based on the neural offset min-sum (NOMS) low-density parity-check (LDPC) decoding network, we propose a neural adaptive offset min-sum (NAOMS) LDPC decoding network. By considering the current input values of the check nodes when learning the additive offset parameters and introducing an adaptive offset weight vector, we can better leverage deep learning techniques. Experimental results show that, with a slight increase in complexity, the proposed decoder achieves a 0.8dB improvement in bit error rate (BER) over the NOMS LDPC decoding network. Moreover, the NAOMS decoder requires fewer learning offset parameters compared to the NOMS decoder.