Robustifying Predictive Intelligence for
A New Spectrum Market

This project is supported by NSF Award #2433966 (2024/10/01-2027/09/30): Robustifying Predictive Intelligence for A New Spectrum Market  

Project Description

From autonomous driving to the metaverse, from digital democracy to intelligent healthcare, our next-generation revolutionizing applications are faced with an unprecedented shortage of frequency spectrum resources to meet their high demands from wireless technologies. This calls for a fundamental paradigm shift of the wireless ecosystem, from the current exclusive usage of licensed spectrum to dynamic, market-driven spectrum utilization that allows free spectrum trading and maximizes spectrum efficiency for a better digital future. As a pillar technology of the new spectrum market, intelligent algorithms based on advanced artificial intelligence and machine learning are expected to provide the ever-needed scalability and adaptability for managing and decision making in the new dynamic and complex environment, working in conjunction with or even replacing traditional algorithms used in the current wireless ecosystem. Building on top of the latest advances from machine learning, robust optimization, economic markets and wireless system design, this project designs robust intelligent algorithms for wireless stakeholders to ensure a reliable and resilient wireless infrastructure and ecosystem that can meet the critical requirements of spectrum access for current and future applications.

Project Personnel and Major Participants

Publications and Preprints

Students receiving funding support from this project are marked with asterisk. PI is underscored in the publications.

• Space Booking: Enabling Performance-Critical Applications in Broadband Satellite Networks [PDF]
  Xiaojian Wang, Ruozhou Yu, Dejun Yang, Guoliang Xue, Qiushi Wei, Huayue Gu, Zhouyu Li,
  Accepted by IEEE International Conference on Distributed Computing Systems (ICDCS), 2025.

• Rank-Based Modeling for Universal Packets Compression in Multi-Modal Communications
  Xuanhao Luo, Zhiyuan Peng, Zhouyu Li, Ruozhou Yu, Yuchen Liu,
  Accepted by IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2025.

• AdaOrb: Adapting In-Orbit Analytics Models for Location-aware Earth Observation Tasks [Artifact]
  Zhouyu Li, Pinxiang Wang, Xiaochun Liang, Xuanhao Luo, Yuchen Liu, Xiaojian Wang, Huayue Gu, Ruozhou Yu,
  Accepted by IEEE International Conference on Pervasive Computing and Communications (PerCom), 2025.

• Infiltrating the Sky: Data Delay and Overflow Attacks in Earth Observation Constellations
  Xiaojian Wang, Ruozhou Yu, Dejun Yang, Guoliang Xue,
  In IEEE International Conference on Network Protocols (ICNP), 2024.

Pre-prints:

• Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks [arXiv]
  Fangtong Zhou*, Xiaorui Liu, Ruozhou Yu, Guoliang Xue,
  arXiv: 2503.24203, pp. 1-12, 2025.

• Moving Edge for On-Demand Edge Computing: An Uncertainty-aware Approach [arXiv]
  Fangtong Zhou*, Ruozhou Yu,
  arXiv: 2503.24214, pp. 1-12, 2025.

• SA2FE: A Secure, Anonymous, Auditable, and Fair Edge Computing Service Offloading Framework [arXiv]
  Xiaojian Wang, Huayue Gu, Zhouyu Li, Fangtong Zhou*, Ruozhou Yu, Dejun Yang, Guoliang Xue,
  arXiv: 2504.20260, pp. 1-12, 2025.

Datasets and Artifacts

• Artifact for paper "AdaOrb: Adapting In-Orbit Analytics Models for Location-aware Earth Observation Tasks" (PerCom'25) is available on Github.

Educational Activities, Outreach and Broader Impacts

• PhD student Zhouyu Li presented AdaOrb at PerCom 2025.

• PI Yu presented a paper on ICNP 2024.