Doctoral Dissertations

Qiaolei Huang

Abstract

"With rapid innovations in Internet of Things (IoT) and wireless technology, more and more consumer electronic devices around the world are now connected to the internet. In a small form factor electronic device, there are plenty of potential noise sources such as System On Chip (SoC), high speed traces, flexible cables and power converters, etc. Those noise sources can possibly introduce radio frequency interference (RFI) issues. In this dissertation, a transfer function based calculation method is proposed to estimate radio frequency interference (RFI) problems. The derived equations can clearly decompose the RFI problem into two parts: the noise source and the coupling transfer function to the antenna. The proposed method is validated through numeric simulations and real cellphone experiments. Based on this method, a novel RFI mitigation method is proposed. Through near-field scanning of a real product, an equivalent dipole moment of the noise source (CPU and DDR3) is reconstructed, and the near-field components of the victim (Wi-Fi antenna) are measured. By determining the relationship between dipole moment and antenna near field, the noise source is rotated by a certain angle to reduce RFI. New boards with the suggested changes are fabricated and the measured results show a good RFI reduction (up to 8 dB) compared to original boards. Novel machine learning method is also introduced to accurately extract equivalent dipole moments from the near field scanning of a noise source. Compared to the conventional least square method, the proposed machine learning based method is believed to have a better accuracy. Also, machine learning based method is more reliable in handling noise in practical applications"--Abstract, page iv.

Advisor(s)

Fan, Jun, 1971-

Committee Member(s)

Beetner, Daryl G.
Hwang, Chulsoon
Khilkevich, Victor
He, Xiaoming

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Sponsor(s)

National Science Foundation (U.S.)

Comments

This dissertation is based upon work supported partially by the National Science Foundation under Grant No. IIP-1440110.

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2019

Journal article titles appearing in thesis/dissertation

  • A transfer function based calculation method for radio frequency interference
  • Efficient RFI mitigation using rotation for DDR noise source
  • Machine learning based source reconstruction for RF desense

Pagination

x, 83 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2019 Qiaolei Huang, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11589

Electronic OCLC #

1119724243

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