An Efficient High-Speed Channel Modeling Method Based on Optimized Design-of-experiment (DoE) for Artificial Neural Network Training


This paper studies the optimized setup in the design-of-experiment (DoE) method to efficiently construct precise artificial neural network (ANN) model for high-speed channel. The accuracy of an ANN model is in general determined by the number of the training sets used in constructing the model. The more the training sets, the better the accuracy is. However, a large number of training sets can significantly increase the effort in obtaining these data (by time-consuming full-wave simulations in the case of channel analysis). Therefore, improving the accuracy while maintaining the same number of training sets by optimizing how the training sets are obtained is critical to develop an efficient ANN construction method. In this paper, different setups in a DoE method, which is used for training data set generation, are studied. Based on error analysis, an optimized setup is proposed. Finally, the performance of the proposed method is validated by the S-parameter simulations with arbitrarily-selected channel parameters within the ranges of interest.


Electrical and Computer Engineering

Research Center/Lab(s)

Electromagnetic Compatibility (EMC) Laboratory


National Science Foundation (U.S.)


This work was supported in part by the National Science Foundation under Grant IIP-1440110.

Keywords and Phrases

Data structures; Error analysis; Fans; Neural networks; Personnel training; Scattering parameters; Artificial neural network models; Channel model; Channel parameter; Construction method; Full-wave simulations; High-speed channels; Training data; Training data sets; Design of experiments; Artificial neural network (ANN) training; Channel modeling; Design-of-experiment (DoE); DoE setup optimization

International Standard Serial Number (ISSN)

0018-9375; 1558-187X

Document Type

Article - Journal

Document Version


File Type





© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2018