基于WBP-CNN算法的LDPC译码

针对低密度奇偶校验(low density parity check, LDPC)码在相关噪声条件下译码误比特率上升的问题,结合传统译码算法与卷积神经网络(convolutional neural network, CNN)设计了新的译码器。该译码器在置信传播(belief propagation, BP)算法中引入加权比特翻转(weighted bit-flipping, WBF)算法,生成加权BP(weighted BP,WBP)结构以解决码字临界处误比特率较高的问题。然后通过CNN降低噪声,在WBP和CNN之间迭代处理接收信号,使信号估计值不断逼近真实值以降低相关噪声的影响。通过仿真发现,与BP算法相比,所提算法能够有效降低相关噪声条件下LDPC译码的误比特率。

LDPC decoding based on WBP-CNN algorithm

Aiming at the problem that the bit error rate of low density parity check(LDPC) codes increases under the condition of correlated noise, a new decoder is designed combined with traditional decoding algorithm and convolutional neural network(CNN). The decoder introduces the weighted bit-flipping(WBF) algorithm into the belief propagation(BP) algorithm to generate a weighted BP(WBP) structure to solve the problem of high bit error rate at the critical point of codeword. Then, the noise is reduced by CNN, and the received signal is processed iteratively between WBP and CNN to make the estimated value of the signal approach the real value continuously, so as to reduce the influence of relevant noise. Simulation results show that compared with BP algorithm, the proposed algorithm can effectively reduce the bit error rate of LDPC decoding under correlated noise.