RINGS: Provably Robust Machine Learning for Next Generation Cellular Networks

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.