Date of Award
8-2022
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical Engineering
Committee Chair/Advisor
Linke Guo
Committee Member
Harlan Russell
Committee Member
Carl Baum
Abstract
As the field of Internet of Things (IoT) continues to grow, a variety of wireless signals fill the ambient wireless environment. These signals are used for communication, however, recently wireless sensing has been studied, in which these signals can be used to gather information about the surrounding space. With the development of 802.11n, a newer standard of WiFi, more complex information is available about the environment a signal propagates through. This information called Channel State Information (CSI) can be used in wireless sensing. With the help of Deep Learning, this work attempts to generate a fingerprinting technique for localizing a ZigBee interference source in the presence of 802.11.
Recommended Citation
Kensler, Dylan, "Deep Learning Based Localization of Zigbee Interference Sources Using Channel State Information" (2022). All Theses. 3853.
https://tigerprints.clemson.edu/all_theses/3853