A DataEfficient Training Model for Signal Integrity Analysis based on Transfer Learning The signal integrity analysis is an essential part of electronic design, while direct high speed signal analysis becomes a timeconsuming work. As machine learning exhibits high performance in communication fields in recent years, deep neural network(DNN) is thought to be a key tool to predict eye diagram metrics. However, DNN based signal integrity analysis faces two challenges: demands for amounts of labelled data and long training period. In this paper, we propose a signal integrity analysis model based on transfer learning. The model makes full use of a trained network and trains networks for various channel environments. To achieve the same predicting accuracy, 64% labelled data are utilized for training compared to DNN. The application of hyperparameters in the neural network improves the prediction accuracy of the eye diagram width and height by 42.7% and 49.24% compared to current methods in few shot signal integrity analysis.

Distortion Element Estimation Technique based on Deep Learning for SelfInterference Cancellation of Full Duplex Communication System This paper presents the deep learningbased distortion element estimation technique of selfinterference signal for DOCSIS 3.1 system with full duplex. In the DOCSIS 3.1 system with full duplex, the selfinterference signal estimation and cancellation are the most important issues to enhance the system performance. Proposed technique estimates channel and nonlinear element of selfinterference signal by using deep learning method. The practical challenge of application of deep learning technique to communication system is that it is difficult to find proper deep neural network structure for a specific purpose of the system. The proposed estimation technique uses simple deep neural network structure to estimate channel and nonlinear element of selfinterference signal in timedomain. The evaluation results of the deep learningbased proposed technique is better than that of existing algorithmbased estimation technique.