{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129421"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129421","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Robust and high performance machine learning for next generation wireless networks","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Liu, Zikun"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Vasisht, Deepak","Choudhury, Romit Roy","Caesar, Matthew","Singh, Gagandeep","Xie, Yaxiong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-23","date_published":"2025-04-23","updated_at":"2026-07-22T22:25:05Z","subjects":["Machine Learning","Wireless Networks","4G","5G","Next Generation Networks","Robust Training","Adversarial Attack","Satellite Network","Time Series Prediction","Sustainability","AI","Articial Intelligence","Wireless Communication","Generative Networks","Transformers","GAN","Variational Autoencoders","LSTM","LLM"],"languages":["en","eng"],"rights":["Copyright 2025 Zikun Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129421","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Vasisht, Deepak","Choudhury, Romit Roy","Caesar, Matthew","Singh, Gagandeep","Xie, Yaxiong"]},{"key":"dc:creator","label":"Author","values":["Liu, Zikun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-23","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Wireless Networks","4G","5G","Next Generation Networks","Robust Training","Adversarial Attack","Satellite Network","Time Series Prediction","Sustainability","AI","Articial Intelligence","Wireless Communication","Generative Networks","Transformers","GAN","Variational Autoencoders","LSTM","LLM"]}]},{"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 2025 Zikun Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129421"]}]},{"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 2025-10-19 without embargo terms","The student, Zikun Liu, accepted the attached license on 2025-04-22 at 02:50.","The student, Zikun Liu, submitted this Dissertation for approval on 2025-04-22 at 03:03.","This Dissertation was approved for publication on 2025-04-23 at 09:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21879 on 2025-10-19 at 18:18:34","Next-generation (NextG) wireless networks promise unprecedented scale, heterogeneity, and capabilities, enabled by rapid advancements in networking hardware such as large-scale antenna arrays, low-earth-orbit (LEO) satellite systems, and edge-computing infrastructures. However, realizing the full potential of these technologies requires addressing significant challenges in system optimization, adaptability, and robustness under real-world constraints. This thesis presents a set of machine learning-based approaches that co-design algorithmic intelligence with emerging wireless hardware to optimize network performance, reliability, and robustness. First, this work designs machine learning-based systems to adaptively leverage new wireless infrastructures, such as satellite networks and 4G/5G base stations, to maximize throughput and reduce communication overhead in volatile environments. These systems incorporate domain-specific insights and predictive modeling to optimize resource allocation and network behavior in real time. Second, this work exposes critical vulnerabilities in existing wireless ML systems by designing practical adversarial attacks that survive over-the-air distortions. We show that these attacks can degrade performance in real deployments, motivating the need for fundamentally more robust solutions. Finally, this thesis discusses future directions, including provably robust learning for wireless systems, energy-efficient techniques to reduce the carbon footprint of NextG infrastructure. By bridging the gap between cutting-edge machine learning and real-world wireless systems, this work contributes toward building robust, efficient, and adaptive networks for the next generation of connectivity"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Robust and high performance machine learning for next generation wireless networks"]}]}],"canonical_facts":{"dc:contributor":["Vasisht, Deepak","Choudhury, Romit Roy","Caesar, Matthew","Singh, Gagandeep","Xie, Yaxiong"],"dc:creator":["Liu, Zikun"],"dc:date":["2025-04-23","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Zikun Liu, accepted the attached license on 2025-04-22 at 02:50.","The student, Zikun Liu, submitted this Dissertation for approval on 2025-04-22 at 03:03.","This Dissertation was approved for publication on 2025-04-23 at 09:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21879 on 2025-10-19 at 18:18:34","Next-generation (NextG) wireless networks promise unprecedented scale, heterogeneity, and capabilities, enabled by rapid advancements in networking hardware such as large-scale antenna arrays, low-earth-orbit (LEO) satellite systems, and edge-computing infrastructures. However, realizing the full potential of these technologies requires addressing significant challenges in system optimization, adaptability, and robustness under real-world constraints. This thesis presents a set of machine learning-based approaches that co-design algorithmic intelligence with emerging wireless hardware to optimize network performance, reliability, and robustness. First, this work designs machine learning-based systems to adaptively leverage new wireless infrastructures, such as satellite networks and 4G/5G base stations, to maximize throughput and reduce communication overhead in volatile environments. These systems incorporate domain-specific insights and predictive modeling to optimize resource allocation and network behavior in real time. Second, this work exposes critical vulnerabilities in existing wireless ML systems by designing practical adversarial attacks that survive over-the-air distortions. We show that these attacks can degrade performance in real deployments, motivating the need for fundamentally more robust solutions. Finally, this thesis discusses future directions, including provably robust learning for wireless systems, energy-efficient techniques to reduce the carbon footprint of NextG infrastructure. By bridging the gap between cutting-edge machine learning and real-world wireless systems, this work contributes toward building robust, efficient, and adaptive networks for the next generation of connectivity"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129421"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Zikun Liu"],"dc:subject":["Machine Learning","Wireless Networks","4G","5G","Next Generation Networks","Robust Training","Adversarial Attack","Satellite Network","Time Series Prediction","Sustainability","AI","Articial Intelligence","Wireless Communication","Generative Networks","Transformers","GAN","Variational Autoencoders","LSTM","LLM"],"dc:title":["Robust and high performance machine learning for next generation wireless networks"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}