RINGS: Provably Robust Machine Learning for Next Generation Cellular Networks

下一代(NextG)网络的规模、多样性和功能将是前所未有的。它们将连接从智能手机到智能传感器的数千亿台设备。这些网络将使非常多样化的设备能够联网-从配备一年电池的低功率物联网(IoT)设备到数据密集型虚拟/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，多天线基站可以感知其环境，并与自动驾驶车辆共享关于行人和汽车的信息。这些特性将使NextG成为许多变革性应用的核心，如数字医疗、工业4.0、自动驾驶和网真。这项提议将构建基于机器学习的强大框架，为下一代网络提供新的通信和感知能力。该计划中的教育努力将培训学生研究和使用尖端数据驱动无线系统。该计划将建立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。具体地说，这些框架将：(A)创建自治系统，消除瓶颈，最大限度地发挥下一代网络中新硬件功能的性能优势，如大型天线阵列和多个频段；以及(B)从无线信号中提取细粒度的见解，用于感知和成像周围环境。该建议的一个关键重点是建立逻辑推理和形式验证框架，为这些机器学习模型的健壮性提供可证明的保证，以便它们对环境和对抗性噪声都具有健壮性。由于NextG基础设施的重要性，这种稳健性对于在生产系统中成功采用数据驱动的方法至关重要。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.