| Summary: | Wireless communications are ubiquitous nowadays, especially in the new era of
Internet of Things (IoT). Most of IoT devices access the Internet via some kind
of wireless connections. The major role of wireless signals is a type of
communication medium. Besides that, taking advantage of the growing physical
layer capabilities of wireless techniques, recent research has demonstrated the
possibility of reusing wireless signals for both communication and sensing. The
capability of wireless sensing and the ubiquitous availability of wireless
signals make it possible to meet the rising demand of pervasive environment
perception. Physical layer features including signal attributes and channel
state information (CSI) can be used for the purpose of physical world sensing.
This dissertation focuses on exploring the sensing capability of wireless
signals. The research approach is to first take measurements from physical layer
of wireless connections, and then develop various techniques to extract or infer
information about the environment from the measurements, like the locations of
signal sources, the motion of human body, etc.
The research work in this dissertation makes three contributions. We start from
wireless signal attributes analysis. Specifically, the cyclostationarity
properties of wireless signals are studied. Taking WiFi signals as an example,
we propose signal cyclostationarity models induced by WiFi Orthogonal Frequency
Division Multiplexing (OFDM) structure including pilots, cyclic prefix, and
preambles. The induced cyclic frequencies is then applied to the
signal-selective direction estimation problem.
Second, based on the analysis of wireless signal attributes, we design and
implement a prototype of a single device system, named MobTrack, which can
locate indoor interfering radios. The goal of designing MobTrack is to provide a
lightweight, handhold system that can locate interfering radios with sub-meter
accuracy with as few antennas as possible. With a small antenna array, the cost,
complexity as well as size of this device are reduced. MobTrack is the
first single device indoor interference localization system without the
requirement of multiple pre-deployed access points (AP).
Third, channel state information is studied in applications of human motion
sensing. We design WiTalk, the first system which is able to do fine-grained
motion sensing like leap reading on smartphones using the CSI dynamics
generated by human movements. WiTalk proposes a new fine-grained human motion
sensing technique with the distinct context-free feature. To achieve this goal
using CSI, WiTalk generates CSI spectrograms using signal processing techniques
and extracts features by calculating the contours of the CSI spectrograms. The
proposed technique is verified in the application scenario of lip reading, where
the fine-grained motion is the mouth movements. === Ph. D.
|
|---|
