RINGS: Ensuring Reliability in mmWave Networks

该项目旨在为多跳毫米波网络设计弹性低复杂度的传输机制，以实现可证明的接近最佳性能并提供服务保证。在无线领域，几个重要的用例依赖于多跳毫米波网络部署，包括专用网络，如购物中心、机场、博物馆和企业;在密集的城市场景中使用毫米波链路作为回程的毫米波网状网络;采用移动的热点的军事应用;以及基于毫米波的车辆到一切（V2X）服务，如协作感知。 尽管毫米波通信具有上述有希望的方面，但众所周知，由于毫米波频谱的不利传播性质，毫米波链路对阻塞高度敏感，信道可能突然改变，路径中断。此外，与传统无线网络一样，由于自然灾害和资源耗尽等因素，毫米波网络节点容易受到组件故障的影响。如果成功，该项目旨在提供新的传输机制，对这种中断具有弹性。为了实现研究的目标，该项目建立在所谓的1-2-1网络模型的基础上，该模型为毫米波网络提供了一个简单但信息丰富的模型。 该项目的工作包括设计主动机制，目的是利用从多路径多样性到多级和网络编码的各种工具，提前建立复原力;设计反应机制，利用反压算法和强化学习等工具，查明和适应链路或节点故障;以及上述机制的综合，以实现能够服务于各种应用的一套工具。将毫米波网络与以图形表示的网络区分开来的一个关键特征是波束调度，它既提供了建立弹性的机会，也带来了主要在操作复杂性方面的挑战。该项目开发的解决方案汇集了多个学科的工具;特别是，它结合了信息论，机器学习，算法和网络工具，以实现拟议的目标。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。

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.