University of Illinois Urbana-Champaign

High-performance wireless perception using deep learning and mems devices

Guan, Junfeng

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GUAN-DISSERTATION-2022.pdf

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https://hdl.handle.net/2142/116231

Description

Title
High-performance wireless perception using deep learning and mems devices
Author(s)
Guan, Junfeng
Issue Date
2022-07-15
Director of Research (if dissertation) or Advisor (if thesis)
Al-Hassanieh, Haitham
Doctoral Committee Chair(s)
Al-Hassanieh, Haitham
Committee Member(s)
  • Roy Choudhury, Romit
  • Patel, Sanjay
  • Valdes Garcia, Alberto
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Wireless Perception
  • Radar Imaging
  • Joint Communication and Sensing
  • Deep Learning
  • MEMS
Abstract
Recent years have witnessed much interest in expanding the use of wireless networks beyond their traditional use for communications to providing new perception solutions, such as sensing, imaging, and localization. The vision is, the wireless perception functionalities of the next generation wireless networks are going to create a digital twin of the physical world. This thesis introduces new software and hardware primitives that advance wireless technologies towards achieving the vision of ubiquitous perception in next-generation wireless networks. The software primitive we introduce is AI-enhanced wireless imaging, where we leverage recent advances in deep neural networks to extract the underlying perceptual and contextual information of the environment from raw wireless images. We demonstrate the applications of AI-enhanced wireless imaging in self-driving car perception, where we develop systems to achieve millimeter-wave radar-based high-resolution imaging and accurate object detection. The hardware primitive we introduce is the first of its kind Micro-Electro-Mechanical System (MEMS) filter hardware, which we leverage to enable joint communication and high-performance sensing in next-generation wireless networks. Towards this end, we first present a spectrum sensing scheme that can efficiently sense wideband spectra with high time resolution. This system can be used to enable dynamic spectrum sharing between perception and communication services in future wireless networks for them to coexist. We also exploit reusing communication signals for perception. We develop an accurate Internet-of-Things (IoT) self-localization system that simply overhears ambient 5G communications signals without any coordination with the base stations in 5G cellular networks.
Graduation Semester
2022-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/116231
Copyright and License Information
Copyright 2022 Junfeng Guan

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