Fast and Precise High-Speed Channel Modeling and Optimization Technique Based on Machine Learning
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.
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
Electrical and Computer Engineering
Electromagnetic Compatibility (EMC) Laboratory
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)
Article - Journal
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