Date of Award


Document Type


Degree Name

Master of Science (MS)


Electrical Engineering

Committee Chair/Advisor

Linke Guo

Committee Member

Harlan Russell

Committee Member

Carl Baum


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.



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