Efficient estimation and mitigation for radio-frequency interference

"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.

Name thesis:degree_name

Ph. D. in Electrical Engineering

Grantor

Missouri University of Science and Technology

Author dc:creator

• Huang, Qiaolei

• Electrical and Computer Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/2806

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-3811