Synopsis |
In
this project, theory and applications of efficient approximations of the
Channel State Information (CSI) in Wi-Fi networks, which describes the key
characteristics of the wireless links, will be developed. The approximation
will exploit the underlying structure of the CSI and will require very few
parameters, resulting in highly effective CSI compression and measurement methods,
as well as new data transmission techniques, which will significantly improve
the efficiency of Wi-Fi networks and enable more applications and more
opportunities in education, public health, and business that increasingly depend
on the high speed and reliability of wireless networks. Fundamental issues that
will be addressed include: finding the theoretical explanation of the
approximation, designing fast CSI compression algorithms, designing more
efficient CSI measurement and prediction methods, and designing novel data
transmission techniques. The knowledge gained in this project will advance the
research field by revealing important features of a large class of wireless
channels that were previously unnoticed, and developing optimized methods for
such channels. Results obtained in this project will be used in classes related
to networking. Both graduate and undergraduate students will participate in
this project, and students from underrepresented and minority groups will be
actively reached out to and recruited.
This project is motivated by an interesting experimental
discovery, which shows that the CSI vectors in Wi-Fi networks can be
approximated very well in many case as the linear combination of very few, such
as 3, sinusoids, even when the number non-negligible paths are much larger. Referring
to channels with such SParse Sinusoid (SPS)
approximation as SPS channels, the goals of this project include: 1)
understanding the theoretical foundation regarding to the existence of the SPS
approximation by studying the mathematical properties of channel and designing
fast CSI compression algorithms, 2) designing efficient CSI measurement and
prediction methods by exploiting underlying structure of the SPS approximation,
and 3) designing new data modulation techniques for SPS channels by exploiting
the simplified representation of the channel. The proposed algorithms and
techniques will be implemented in experimental platforms and tested in
real-world wireless channels. By removing
the bottleneck caused by the high overhead in CSI feedback and measurement, the
outcomes of this research will be timely solutions for Wi-Fi networks for better
supporting MU-MIMO or massive MIMO. The new data modulation techniques
for SPS channels will likely improve the link speed while reducing the
complexity.