基于深度学习的实用HDPC码译码方法研究

针对现有高密度校验码量化译码性能问题，本文提出了一种基于深度学习的量化最小和译码算法-QMSND.借助深度神经网络，通过对神经最小和译码信道输入向量和每轮迭代过程中节点更新信息进行非均匀间隔量化，动态调整Tanner图边的权重参数，改善消息传播效能.计算机仿真实验结果表明，本文提出的方法在对BCH码进行译码时仅需要8比特表示信息即可接近未经量化的浮点译码性能.因此，所提出的QMSND译码方法便于硬件实现，具有一定的实用性.

Research on Practical Method for Decoding to HDPC Codes via Deep Learning

Aiming at the problem of quantitative decoding performance of high-density parity-check codes, this study proposes a quantitative min-sum decoding approach based on deep learning, referenced as QMSND. In order to improve the decoding performance and efficiency, the proposed decoder can adjust the weight parameters of a Tanner graph dynamically, and quantize the input vector and the message of nodes at every iteration in nonuniform fashion via deep neural network. Computer simulation results show that the performance of proposed QMSND decoding with 8-bits presentation is very close to that of the float neural min-sum decoding without quantization. Therefore, the proposed decoding approach is easy to implement by hardware and has some practicability.