A new LDPC decoding scheme based on BP and Gated Neural Network

Abstract

In long wave communication scenarios, channel interferences lead to noise correlation. Since traditional belief propagation (BP) decoding algorithm is designed based on additive white Gaussian noise (AWGN), a performance cost will appear when it comes to correlated noise. In this paper, a new LDPC decoding scheme that applies to correlated Gaussian noise is proposed. We design a BP-gated neural network-BP structure to carry on two rounds of BP decoding with the second round based on optimized noise. By adopting gated neurons in typical NN, CNN, training performance is improved. Simulation shows that compared with BP decoding algorithm, the new decoding scheme outperforms traditional method by 0.5∼0.61 dB when bit error rate is 10–6. This decoding scheme also works on pectinate noise with performance gain of 1.5dB.