PointNet-Based Jitter Decomposition on Point Cloud of Jitter Histogram

Abstract

Jitter is one of the key factors affecting bit error rate (BER) on high-speed links. A novel method of jitter decomposition by PointNet using 2D point cloud of jitter histogram is proposed for decomposing the time interval error (TIE) jitter into deterministic jitter (DJ) and random jitter (RJ). The proposed method uses transformNet (T-Net) and multi-layer perceptron (MLP) to learn global point cloud features and uses average pooling to aggregate information from all the points. Experimental results show that the proposed approach can decompose jitter into DJ and RJ, and is better than the traditional jitter decomposition method i.e. time lag correlation (TLC) and Time-Domain PLL Jitter Decomposition (T-D PLL). In addition, results show that the performance of the proposed method is better than CNN, PointRNN, and PointANN.