NSF-IITP: AI/ML-Enabled Scalable and Privacy-Preserving 6G Space-Air-Ground Integrated Network Operation

University Of Maryland Baltimore County, Baltimore MD

Investigators

Abstract

The 6th generation (6G) wireless technology is envisaged to provide hyperconnectivity across humans, machines, and sensors, fueling the growth of exciting new applications in expanded reality (XR), artificial intelligence (AI), and autonomous robotics, to name a few. This project focuses on the key enabling technology, namely, the integrated non-terrestrial networks (NTNs), which encompass space, air, and ground components, such as the low Earth orbit (LEO) satellites, high-altitude platform stations (HAPSs), and unmanned aerial vehicles (UAVs), in addition to the traditional terrestrial stations. Freeing itself from fixed locations, the space-air-ground integrated network can support seamless connectivity to remote regions (e.g. for climate monitoring), disaster areas, hot spots, and coverage holes, as well as high-mobility clusters such as aircrafts and vessels. Significant technical challenges emerge, however, with such a flexible network architecture. This project aspires to explore novel solutions to critical operational issues of NTNs, by tapping into powerful AI and machine learning (ML) techniques. Notably, the proposed research is designed to benefit from close collaboration among the participating US and South Korean institutions. The research outcomes will substantially advance the theory and practice of 6G integrated networking, secure global technological leadership of the US/Korean workforce, and contribute to societal and environmental agenda by providing vital infrastructure to combat the critical issues therein. The gained knowledge will have impact to other science and technology domains as well, such as network science, data science, distributed robotics, and privacy-preserving smart health. The attendant educational components will provide fresh learning experiences suitable for preparing STEM talents in the US and South Korea. More specifically, the project aims at addressing key challenges associated with NTN operation, ranging from radio environment analysis, space-air-ground integrated routing, multi-satellite coordination, service-aware resource allocation, to privacy protection. While recent advances in AI/ML is expected to be the opportune enabler for this endeavor, it is observed that to ensure efficiency and robustness in the training and operation of the AI/ML modules, traditional data-driven black box approaches need to be complemented with proven domain-specific paradigms and novel ML architectural insights. In this context, diverse expertise in ML, signal processing, communication, networking, and information theory will be pooled together through tight international collaboration to make transformative contributions. Important research agenda to be explored include: 1) Complex radio environment cartography through joint ML and signal processing; 2) scalable network optimization via constrained multi-agent reinforcement learning; and 3) fundamental trade-offs in privacy-preserving inference over wireless networks. Furthermore, integrative research of these agenda will be pursued to devise map-assisted network control methods for highly dynamic NTN scenarios and privacy-preserving map inference and multi-agent coordination schemes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.