{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116231"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116231","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"High-performance wireless perception using deep learning and mems devices","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Guan, Junfeng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Al-Hassanieh, Haitham","Roy Choudhury, Romit","Patel, Sanjay","Valdes Garcia, Alberto"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["Wireless Perception","Radar Imaging","Joint Communication and Sensing","Deep Learning","MEMS"],"languages":["en","eng"],"rights":["Copyright 2022 Junfeng Guan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116231","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Al-Hassanieh, Haitham","Roy Choudhury, Romit","Patel, Sanjay","Valdes Garcia, Alberto"]},{"key":"dc:creator","label":"Author","values":["Guan, Junfeng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-15"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Wireless Perception","Radar Imaging","Joint Communication and Sensing","Deep Learning","MEMS"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Junfeng Guan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116231"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["High-performance wireless perception using deep learning and mems devices"]}]}],"canonical_facts":{"dc:contributor":["Al-Hassanieh, Haitham","Roy Choudhury, Romit","Patel, Sanjay","Valdes Garcia, Alberto"],"dc:creator":["Guan, Junfeng"],"dc:date":["2022-08","2022-07-15"],"dc:description":["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","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116231"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Junfeng Guan"],"dc:subject":["Wireless Perception","Radar Imaging","Joint Communication and Sensing","Deep Learning","MEMS"],"dc:title":["High-performance wireless perception using deep learning and mems devices"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}