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

第六代（6 G）无线技术旨在提供跨人类、机器和传感器的超连接，推动扩展现实（XR）、人工智能（AI）和自主机器人等令人兴奋的新应用的增长。该项目的重点是关键的使能技术，即综合非地面网络，除传统的地面站外，还包括空间、空中和地面组成部分，如低地球轨道卫星、高空平台站和无人驾驶飞行器。从固定位置中解放出来，空间-空中-地面综合网络可以支持与偏远地区（例如气候监测），灾区，热点和覆盖漏洞以及飞机和船舶等高移动性集群的无缝连接。然而，这种灵活的网络架构带来了重大的技术挑战。该项目旨在通过利用强大的AI和机器学习（ML）技术，探索NTN关键运营问题的新解决方案。值得注意的是，拟议的研究旨在受益于参与美国和韩国机构之间的密切合作。研究成果将大大推进6 G综合网络的理论和实践，确保美国/韩国劳动力的全球技术领导地位，并通过提供重要的基础设施来解决其中的关键问题，为社会和环境议程做出贡献。所获得的知识也将对其他科学和技术领域产生影响，例如网络科学，数据科学，分布式机器人和隐私保护智能健康。具体而言，该项目旨在解决NTN运营相关的关键挑战，包括无线电环境分析、空间-空中-地面综合路由、多卫星协调、服务感知资源分配以及隐私保护。虽然人工智能/机器学习的最新进展有望成为这一奋进的有利推动因素，但据观察，为了确保人工智能/机器学习模块的训练和操作的效率和鲁棒性，传统的数据驱动的黑盒方法需要用经过验证的特定领域范例和新颖的机器学习架构见解来补充。在此背景下，机器学习、信号处理、通信、网络和信息理论方面的各种专业知识将通过紧密的国际合作汇集在一起，以做出变革性的贡献。需要探索的重要研究议程包括：1）通过联合ML和信号处理进行复杂无线电环境制图; 2）通过约束多代理强化学习进行可扩展网络优化; 3）在无线网络上进行隐私保护推理的基本权衡。此外，这些议程的综合研究将追求设计地图辅助网络控制方法的高度动态NTN场景和隐私保护的地图推理和多智能体协调schemes.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.