Decoding Quadratic Residue Codes Using Deep Neural Networks
Decoding Quadratic Residue Codes Using Deep Neural Networks
复制标题
使用深度神经网络解码二次余数代码
作者：
Ming Wang;Yong Li;R. Liu;Huihui Wu;Youqiang Hu;F. Lau
在本文中，提出了一种基于神经网络的低复杂度解码器来解码二进制二次剩余（QR）码。所提出的解码器基于神经最小和算法和改进的随机冗余解码器（mRRD）算法。这种新方法具有与最小和算法相同的渐近时间复杂度，远低于校正子差（DS）算法。仿真结果表明，与DS算法相比，该算法获得了超过0.4 dB的增益。此外，应用基于捕获集的简化方法来降低 mRRD 的复杂性。这种简化会导致错误性能略有损失并降低实现复杂性。
In this paper, a low-complexity decoder based on a neural network is proposed to decode binary quadratic residue (QR) codes. The proposed decoder is based on the neural min-sum algorithm and the modified random redundant decoder (mRRD) algorithm. This new method has the same asymptotic time complexity as the min-sum algorithm, which is much lower than the difference on syndromes (DS) algorithm. Simulation results show that the proposed algorithm achieves a gain of more than 0.4 dB when compared to the DS algorithm. Furthermore, a simplified approach based on trapping sets is applied to reduce the complexity of the mRRD. This simplification leads to a slight loss in error performance and a reduction in implementation complexity.