[PAST EVENT] Yongsen Ma, Computer Science - Oral exam
In recent years, WiFi has a very rapid growth due to its high throughput, high
efficiency, and low costs. Multiple-Input Multiple-Output (MIMO) and
Orthogonal Frequency-Division Multiplexing (OFDM) are two key technologies for
providing high throughput and efficiency for WiFi systems. MIMO-OFDM
provides Channel State Information (CSI) which represents the amplitude
attenuation and phase shift of each transmit-receiver antenna pair of each carrier
frequency. CSI helps WiFi achieve high throughput to meet the growing demands
of wireless data traffic. CSI captures how wireless signals travel through the
surrounding environment, so it can also be used for wireless sensing purposes. This
dissertation presents how to improve WiFi sensing and networking with CSI.
For WiFi sensing, there are many wireless sensing applications using CSI as the
input in recent years. To get a better understanding of existing WiFi sensing
technologies and future WiFi sensing trends, this dissertation presents a survey of
signal processing techniques, algorithms, applications, performance results,
challenges, and future trends of CSI-based WiFi sensing. CSI is widely used for
gesture recognition and sign language recognition. Existing methods for
WiFi-based sign language recognition have low accuracy and high costs when there
are more than 200 sign gestures. The dissertation presents SignFi for sign
language recognition using CSI and Convolutional Neural Networks (CNNs).
SignFi provides high accuracy and low costs for run-time testing for 276 sign
gestures in the lab and home environments.
For WiFi networking, although CSI provides high throughput for WiFi networks,
it also introduces high overhead. WiFi transmitters need CSI feedback for transmit
beamforming and rate adaptation. The size of CSI packets is very large and it
grows very fast with respect to the number of antennas and channel width. CSI
feedback introduces high overhead which reduces the performance and efficiency of
WiFi systems, especially mobile and hand-held WiFi devices. This dissertation
presents RoFi to reduce CSI feedback overhead based on the mobility status of
WiFi receivers. CSI feedback compression reduces overhead, but WiFi receivers
still need to send CSI feedback to the WiFi transmitter. The dissertation presents
EliMO for eliminating CSI feedback without sacrificing beamforming gains.
Yongsen Ma is a Ph.D. candidate at William & Mary, supervised by Dr. Gang Zhou. He got his M.S. degree from Shanghai Jiao Tong University and B.S. degree from Shandong University. His research interests include wireless networks, wireless sensing, and applying deep learning in wireless systems.