2026-09-24T03:23:06Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/116231 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Al-Hassanieh, Haitham Al-Hassanieh, Haitham Roy Choudhury, Romit Patel, Sanjay Valdes Garcia, Alberto 2022-08 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms The student, Junfeng Guan, accepted the attached license on 2022-07-14 at 17:52. The student, Junfeng Guan, submitted this Dissertation for approval on 2022-07-15 at 12:38. This Dissertation was approved for publication on 2022-07-15 at 14:11. DSpace SAF Submission Ingestion Package generated from Vireo submission #18288 on 2022-11-15 at 18:20:53 High-performance wireless perception using deep learning and mems devices Guan, Junfeng 2022-07-15 Wireless Perception Radar Imaging Joint Communication and Sensing Deep Learning MEMS 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. Thesis eng https://hdl.handle.net/2142/116231 Copyright 2022 Junfeng Guan Ph.D. Dissertation Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng