{"681563":{"#nid":"681563","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Jingyuan Zhang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; Enhancing mmWave Network Connectivity and Edge Intelligence through Reconfigurable Intelligent Surfaces for Communication and Edge Processing\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Douglas Blough, ECE, Chair, Advisor\u003C\/p\u003E\u003Cp\u003EDr. Karthikeyan Sundaresan, ECE\u003C\/p\u003E\u003Cp\u003EDr. Ragupathy Sivakumar, ECE\u003C\/p\u003E\u003Cp\u003EDr. Nima Ghalichechian, ECE\u003C\/p\u003E\u003Cp\u003EDr. Mostafa Ammar, CoC\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe next generation of wireless networks needs to support massive device deployments and ensure robust, widespread connectivity, particularly for edge devices in various Internet-of-Things (IoT) applications. To achieve this goal, two primary requirements should be addressed: (1) maintaining strong and reliable signal connectivity for a large number of devices, and (2) enabling real-time, on-device data processing for edge devices. To address signal connectivity, mmWave technology offers high throughput but suffers from limited propagation and high penetration loss, particularly in non-line-of-sight (NLOS) scenarios. This dissertation explores the use of reconfigurable intelligent surfaces (RISs) to optimize mmWave signal coverage. In particular, a stochastic geometry-based framework is utilized to evaluate the performance of mmWave communication assisted by multi-RIS links, considering both reflective and transmissive-reflective RISs. Additionally, multi-RIS deployment strategies are proposed to enhance indoor coverage by optimizing RIS placement using stochastic or full obstacle information. For on-device data processing, this dissertation investigates RIS-based over-the-air (OTA) computation, with the aim to reduce memory and computational demands by offloading computations from digital processors to the radio frequency (RF) domain. This dissertation investigates applications including a low-complexity direction-of-arrival (DoA) estimator leveraging RISs and RIS-based RF neural networks for machine learning inference in RF domain. Two RF network architectures are explored: fully connected layers with quantized RIS phase configurations and RF convolutional layers. Simulation results validate both applications, demonstrating the potential of RISs in enhancing wireless network efficiency.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Enhancing mmWave Network Connectivity and Edge Intelligence through Reconfigurable Intelligent Surfaces for Communication and Edge Processing "}],"uid":"28475","created_gmt":"2025-04-03 17:47:09","changed_gmt":"2025-04-03 17:48:16","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-15T12:30:00-04:00","event_time_end":"2025-04-15T14:30:00-04:00","event_time_end_last":"2025-04-15T14:30:00-04:00","gmt_time_start":"2025-04-15 16:30:00","gmt_time_end":"2025-04-15 18:30:00","gmt_time_end_last":"2025-04-15 18:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Room 1202, Klaus","extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}