Research Project

Understanding and Design of Generative Learning-based Mobile and Wireless Network Systems

 

• Project Information

  • Principal investigator: Dr. Yuchen Liu
  • Graduate students: Zhizhen Li, Xuanhao Luo

   

• Project Overview

  • This project explores the application of generative learning techniques to enhance the efficiency, reliability, and adaptability of mobile and wireless networks. The project delves into how machine learning and optimization approaches can be employed to revolutionize various network operations, including signal processing, channel analysis, resource allocation, and network management. By leveraging the ability of generative models and time-series machine learning framework to learn complex patterns and distributions from data, the project aims to address challenges in data-sparsity, dynamic and heterogeneous network environments. The approach not only improves performance but also enables networks to better handle unpredictable conditions, leading to more robust and intelligent wireless communication systems.

   

• Publications

  • Z. Peng, Y. Liu, G. Li, Z. Yang, M. Chen; D. Xu, and X. Lin, ‘‘Generative Artificial Intelligence Models for Emerging Communication Systems: Fundamentals and Challenges’’, IEEE Communications Magazine (COMMAG), to appear, 2025.

  • X. Luo, Z. Li, Z. Peng, M. Chen, and Y. Liu, ‘‘Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks’’, IEEE Transactions on Cognitive Communications and Networking (TCCN) [J], 2025.

  • Y. Liu, S. Li, X. Lin, X. Chen, G. Li, Y. Liu, B. Liao, and J. Li, ‘‘QoS-Aware Multi-AIGC Service Orchestration at Edges: An Attention-Diffusion-Aided DRL Method’’, IEEE Transactions on Cognitive Communications and Networking (TCCN) [J], 2025.

  • Y. Cai, Z. He, Y. Luo, J. Cao, Y. Liu, Z. Luo, and S. Zhao, ‘‘Adversarial Attacks on Cooperative Spectrum Sensing: A LLM-Powered Multi-Agent Approach’’, IEEE Workshop on Signal Processing and Artificial Intelligence for Wireless Communications (SPAWC), to appear, 2025.

  • D. Wu, Z. Peng, M. Chen, and Y. Liu, ‘‘Transforming Network Intrusion Detection Using Large Language Models’’, Proc. of IEEE Consumer Communications & Networking Conference (CCNC), 2025.

  • Z. Li, M. Chen, G. Li, X. Lin, and Y. Liu, ‘‘Map-Driven mmWave Link Quality Prediction with Spatial-Temporal Mobility Awareness’’, IEEE Transactions on Mobile Computing (TMC) [J], 2024.

  • J. Liu, Z. Peng, D. Xu, and Y. Liu, ‘‘Revolutionizing Wireless Modeling and Simulation with Network-Oriented LLMs’’, Proc. of 43rd IEEE International Performance Computing and Communications Conference (IPCCC), 2024 (🏆 Best Paper Award Runner-up).

  • X. Luo, Z. Li, Z. Peng, D. Xu, and Y. Liu, “RM-Gen: Conditional Diffusion Model-Based Radio Map Generation for Wireless Networks”, IFIP/IEEE Networking, 2024.

  • Z. Li, X. Luo, M. Chen, C. Xu, and Y. Liu, “Context-Aware Beam Management via Online Probing in Combinatorial Multi-Armed Bandits”, IEEE International Conference on Communications (ICC), 2024.

  • H. Yu, M. Chen, Z. Yang, and Y. Liu, “Complex Neural Networks for Indoor Positioning with Complex-Valued Channel State Information”, IEEE Global Communications Conference (GLOBECOM), 2023.

   

• Education, Outreach and Broader Impacts

  • Integrating Research & Teaching: The introduction of machine learning for wireless has been added in the newly developed course CSC/ECE Advanced NextG Network Design at both Department of Computer Science and Department of Electrical and Computer Engineering, North Carolina State University.

  • Open-source Toolsets: 1) AutoSim – An interactive, automated, and script-free simulation tool using Network-oriented LLMs. Link Demo ns-3-LLMs; 2) LLM-IDS – A platform for enhancing network intrusion detection by integrating large language models to improve attack detection, analysis, and interpretability. Link.

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