RINGS: Ensuring Reliability in mmWave Networks

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

This project aims to design resilient low-complexity transmission mechanisms for multi-hop mmWave networks that achieve provably near-optimal performance and provide service assurances. Within the wireless landscape, several important use cases rely on multihop mmWave network deployments, including private networks, such as in shopping centers, airports, museums and enterprises; mmWave mesh networks that use mmWave links as backhaul in dense urban scenarios; military applications employing mobile hotspots; and mmWave based vehicle-to-everything (V2X) services, such as cooperative perception. Despite the aforementioned promising aspects of mmWave communication, it is well known that -- because of the hostile propagation nature of the mmWave spectrum -- mmWave links are highly sensitive to blockage, channels may abruptly change and paths get disrupted. Moreover, as in traditional wireless networks, mmWave network nodes are susceptible to component failures, due to factors such as natural hazards and resource depletion. If successful, this project aims to provide novel transmission mechanisms that are resilient against such disruptions. Towards achieving the objectives of the research, the project builds on the so-called 1-2-1 network model that offers a simple yet informative model for mmWave networks. The project work includes the design of proactive mechanisms, that aim to build resilience in advance by exploiting tools that range from multipath diversity to multilevel and network coding; the design of reactive mechanisms, that identify and adapt to link or node failures, using tools such as backpressure algorithms and reinforcement learning; and synthesis of the above mechanisms to enable a suite of tools that can serve a variety of applications. A key feature that differentiates mmWave networks from networks that are represented as graphs is beam scheduling, which offers both opportunities to build resilience, as well as challenges mainly in terms of complexity of operation. The project develops solutions that bring together tools from several disciplines; in particular, it combines tools from information theory, machine learning, algorithms, and networking to address the proposed goals. 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.