Title

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.

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Electromagnetic Compatibility (EMC) Laboratory

Sponsor(s)

National Science Foundation (U.S.)

Comments

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

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.

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