Jitter is one of the main factors affecting the bit error rate, and jitter decomposition is a crucial tool with which to characterize jitter at a given BER. In this article, we address this problem based on PointNet and propose a PointNet-based dual-Dirac model (PointNet-DD), where its input is the two-dimensional point cloud formed by the coordinate of the jitter histogram. In particular, we develop a feature extractor, where the stride size of the one-dimensional convolution layer of PointNet is changed to better learn the local features, the corresponding variance and mean features hidden under the jitter histogram point cloud are extracted by global max pooling and global average pooling, respectively. Then, we introduce a dual-Dirac model in the network for jitter calculation to make the estimated deterministic jitter and random jitter more accurate. Consequently, the PointNet-DD can improve the mean absolute error of jitter decomposition. Finally, this approach is practically tested on the test circuits. Experimental results show that the performance, robustness, space, and time of the proposed method are better than other methods.

中文翻译：

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基于 PointNet 的双狄拉克模型的抖动分解


抖动是影响误码率的主要因素之一，而抖动分解是表征给定 BER 下抖动的重要工具。在本文中，我们基于PointNet解决这个问题，提出了一种基于PointNet的双狄拉克模型（PointNet-DD），其输入是由抖动直方图的坐标形成的二维点云。特别是，我们开发了一个特征提取器，其中改变PointNet一维卷积层的步幅大小以更好地学习局部特征，通过全局最大池化提取隐藏在抖动直方图点云下的相应方差和均值特征和全局平均池化。然后，我们在网络中引入双狄拉克模型进行抖动计算，使得估计的确定性抖动和随机抖动更加准确。因此，PointNet-DD可以改善抖动分解的平均绝对误差。最后，在测试电路上对该方法进行了实际测试。实验结果表明，该方法的性能、鲁棒性、空间和时间均优于其他方法。