Fast and Precise High-Speed Channel Modeling and Optimization Technique Based on Machine Learning
Abstract
This letter proposes a fast and precise high-speed channel modeling and optimization technique based on machine learning algorithms. Resistance, inductance, conductance, and capacitance (RLGC) matrices of a high-speed channel are precisely modeled by design-of-experiment method and artificial neural network. In addition, an optimal channel design, which achieves minimum channel loss and crosstalk, is investigated within short time by a genetic algorithm. The performance of the proposed technique is validated by simulations up to 20 GHz.
Recommended Citation
H. Kim et al., "Fast and Precise High-Speed Channel Modeling and Optimization Technique Based on Machine Learning," IEEE Transactions on Electromagnetic Compatibility, vol. 60, no. 6, pp. 2049 - 2052, Institute of Electrical and Electronics Engineers (IEEE), Dec 2018.
The definitive version is available at https://doi.org/10.1109/TEMC.2017.2782704
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Electromagnetic Compatibility (EMC) Laboratory
Sponsor(s)
National Science Foundation (U.S.)
Keywords and Phrases
Data structures; Design of experiments; Genetic algorithms; Learning systems; Matrix algebra; Neural networks; Numerical methods; Numerical models; Optimization; Channel model; Latin hypercube sampling; Symmetric matrices; Training data; Transmission line matrix methods; Learning algorithms; Artificial neural network (ANN); Channel modeling and optimization; Genetic algorithm (GA); Latin-hypercube sampling (LHS)
International Standard Serial Number (ISSN)
0018-9375; 1558-187X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Dec 2018
Comments
This work was supported in part by the National Science Foundation (NSF) under Grant IIP-1440110.