RINGS: Provably Robust Machine Learning for Next Generation Cellular Networks

RINGS：可证明稳健的下一代蜂窝网络机器学习

• 批准号: 2148583
• 负责人: Deepak Vasisht
• 金额: $ 79.4万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148583&HistoricalAwards=false
• 关键词: RINGS; Provably; Robust; Machine; Learning

Next-generation (NextG) networks will be unprecedented in their scale, diversity, and capabilities. They will connect hundreds of billions of devices ranging from smartphones to smart sensors. These networks will enable networking for very diverse devices -- low power Internet-of-things (IoT) devices with year-long batteries to data hungry virtual/augmented reality (VR/AR) headsets. Finally, next-generation networks will enable new services through joint communication and sensing -- e.g., a multi-antenna base station may sense its environment and share information about pedestrians and cars with autonomous vehicles. These characteristics will make NextG central to many transformative applications like digital healthcare, Industry 4.0, autonomous driving, and telepresence. This proposal will build robust Machine Learning-based frameworks that deliver new communication and sensing capabilities for NextG networks. The educational efforts in this proposal will train students to research and work with cutting edge data-drive wireless systems.The proposal will build state-of-the-art Machine Learning frameworks that will be key enablers for Next-generation (NextG) networks. Specifically, these frameworks will: (a) create autonomous systems that remove bottlenecks and maximize the performance benefits of novel hardware capabilities in NextG networks such as massive antenna arrays, and multiple frequency bands, and (b) extract fine-grained insights from wireless signals for sensing and imaging of the surrounding environment. A key focus of this proposal is to build logical reasoning and formal verification frameworks that provide provable guarantees on the robustness of these Machine Learning models, so that they are robust to both environmental and adversarial noise. Such robustness is crucial for successful adoption of data-driven approaches in production systems, due to the criticality of NextG infrastructure.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.

下一代（NextG）网络在规模、多样性和功能方面都将是前所未有的。它们将连接从智能手机到智能传感器的数千亿台设备。这些网络将为各种各样的设备提供网络连接--低功耗物联网（IoT）设备，电池长达一年，数据饥渴的虚拟/增强现实（VR/AR）耳机。最后，下一代网络将通过联合通信和传感实现新的服务，例如，多天线基站可以感测其环境并与自动车辆共享关于行人和汽车的信息。这些特性将使NextG成为数字医疗、工业4.0、自动驾驶和网真等许多变革性应用的核心。该提案将构建强大的基于机器学习的框架，为NextG网络提供新的通信和传感功能。 该提案中的教育工作将培训学生研究和使用尖端的数据驱动无线系统。该提案将构建最先进的机器学习框架，这些框架将成为下一代（NextG）网络的关键推动因素。具体而言，这些框架将：（a）创建自主系统，消除瓶颈并最大限度地提高NextG网络中新型硬件功能的性能优势，例如大规模天线阵列和多个频带，以及（B）从无线信号中提取细粒度的洞察力，用于周围环境的感知和成像。该提案的一个重点是构建逻辑推理和形式验证框架，为这些机器学习模型的鲁棒性提供可证明的保证，使它们对环境和对抗性噪声都具有鲁棒性。由于NextG基础设施的关键性，这种稳健性对于在生产系统中成功采用数据驱动方法至关重要。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

BatMobility: Towards Flying Without Seeing for Autonomous Drones

BatMobility：迈向无视飞行的自主无人机

• 发表时间: 2023
• 期刊: Proceedings of the annual International Conference on Mobile Computing and Networking
• 作者: Sie, Emerson;Liu, Zikun;Vasisht, Deepak
• 通讯作者: Vasisht, Deepak

Scalable verification of GNN-based job schedulers

• DOI: 10.1145/3563325
• 发表时间: 2022-03
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Haoze Wu;Clark W. Barrett;Mahmood Sharif;Nina Narodytska;Gagandeep Singh

Provable Defense Against Geometric Transformations

• 发表时间: 2022-07
• 作者: Rem Yang;Jacob S. Laurel;Sasa Misailovic;Gagandeep Singh

Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems

探索基于机器学习的无线系统的实际漏洞

• 发表时间: 2023
• 期刊: USENIX Symposium on Networked Systems Design and Implementation
• 作者: Liu, Zikun;Xu, Changming;Sie, Emerson;Singh, Gagandeep;Vasisht, Deepak
• 通讯作者: Vasisht, Deepak