Robust and high performance machine learning for next generation wireless networks

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

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Liu, Zikun

Contributors dc:contributor

• Vasisht, Deepak
• Choudhury, Romit Roy
• Caesar, Matthew
• Singh, Gagandeep
• Xie, Yaxiong

• 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

Statement dc:rights

• Copyright 2025 Zikun Liu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129421

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129421