NSF-BSF: CNS Core: Small: Improving Wireless Networks Robustness via Weather-Sensitive Predictive Management

NSF-BSF：CNS 核心：小型：通过天气敏感预测管理提高无线网络的稳健性

• 批准号: 1910757
• 负责人: Gil Zussman
• 金额: $ 40万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910757&HistoricalAwards=false
• 关键词: NSF; BSF; CNS; Core; Small

This project focuses on the wireless networks that are used in the backbone of cellular, smart cities, and emerging 5G networks. These networks rely on millimeter-wave (mmWave) frequencies, which are sensitive to weather conditions and specifically to rain events. Improving resilience to such events, the rapid increase in wireless traffic, and the Quality of Service (QoS) demands of mission-critical smart city applications, all call for dynamic network management schemes. Therefore, weather-sensitive network control and management approaches will be developed, aiming to improve network resilience and performance. The innovation of this project is to use weather-affected measurements of wireless link states in the network to accurately predict their future states and to provide input to network control schemes. These schemes include adjustments of links' modes and network topology to the moving rain, prior to its effects on the signals. The algorithms designed will build on extensive datasets of wireless links and the algorithms will be evaluated and demonstrated in the National Science Foundation-funded COSMOS platform for advanced wireless research (PAWR) testbed. The project will have a strong outreach component, including programs for Harlem public school teachers and Israeli K-12 students. On a societal scale, the development of algorithms that enhance network resilience in face of weather events can improve network connectivity in cases where it is most needed (e.g., emergency situations). In general, enhancing the performance of future smart city and 5G networks will help bridge the digital divide and bringing better connectivity to under-served communities.Specifically, the project focuses on backhaul and fronthaul networks which are currently transitioning to E-band (60-90 gigahertz) links that are very sensitive to rain events. Contrary to legacy (4G) cellular networks where local physical layer adaptation has been sufficient, in emerging smart city and 5G networks (that will require low latency and high bandwidth), link and network layer adaptations will be essential. Algorithms that use the self-extracted attenuation measurements from the network to predict the channel states throughout the network will be developed based on relationships between weather and signal attenuation. Then, weather-sensitive cross-layered control algorithms will be developed. These algorithms will jointly optimize power, modulation and coding, channel allocation, and routing to satisfy QoS requirements in response to predicted changes in network conditions. Finally, the project's contributions will include analysis of first-of-their-kind mmWave backhaul measurements from a smart city network in Israel and unique evaluation in a city-scale test-bed that integrates first-of-their-kind mmWave transceivers.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.

该项目着重于蜂窝，智能城市和新兴5G网络的骨架中使用的无线网络。这些网络依赖于毫米波（MMWAVE）频率，这些频率对天气条件，特别是雨活动敏感。提高对此类事件的韧性，无线流量的迅速增加以及服务至关任性的智能城市应用程序的服务质量（QoS）要求，都要求动态网络管理方案。因此，将开发对天气敏感的网络控制和管理方法，旨在提高网络的弹性和性能。该项目的创新是使用网络中无线链路状态的天气影响的测量值，以准确预测其未来状态并为网络控制方案提供投入。这些方案包括对链接模式和网络拓扑的调整，并在其对信号的影响之前。设计的算法将在无线链路的广泛数据集上构建，并将在国家科学基金会资助的高级无线研究（PAWR）测试台上评估和证明算法。该项目将拥有强大的外展部分，包括针对哈林公立学校教师和以色列K-12学生的计划。在社会规模上，面对天气事件，增强网络弹性的算法的发展可以在最需要的情况下（例如紧急情况）提高网络连接。通常，提高未来智能城市和5G网络的性能将有助于弥合数字鸿沟，并为服务不足的社区带来更好的连接性。具体来说，该项目着重于反式路和弗朗索尔网络，这些网络目前正在过渡到E-Band（60-90 Gigahertz）链接，这些链接对雨活动非常敏感。与新兴的智能城市和5G网络（需要低延迟和高带宽）中，局部物理层适应已经足够的遗产（4G）蜂窝网络相反，链接和网络层的适应至关重要。使用网络中自我提取的衰减测量测量来预测整个网络中的通道状态的算法将根据天气和信号衰减之间的关系开发。然后，将开发对天气敏感的跨层控制算法。这些算法将共同优化功率，调制和编码，通道分配以及路由以满足QoS要求，以响应网络条件的预测变化。最后，该项目的贡献将包括对以色列的智能城市网络进行首次奖励MMWave回程测量的分析，并在城市规模的测试床中进行了独特的评估，该测试床整合了首先是现实的MMWave收发器。本奖项奖均反映了NSF的法定任务，并通过评估了范围的知识群体，反映了NSF的法定任务，并已被评估范围。

项目成果

Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

• DOI: 10.1145/3570616
• 发表时间: 2022-03
• 期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
• 作者: I. Kadota;Dror Jacoby;H. Messer;G. Zussman;J. Ostrometzky

Real-Time Video Anonymization in Smart City Intersections

• DOI: 10.1109/mass56207.2022.00078
• 发表时间: 2022-10
• 期刊: 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Alex Angus;Zhuoxu Duan;G. Zussman;Z. Kostić

Short-Term Prediction of the Attenuation in a Commercial Microwave Link Using LSTM-based RNN

使用基于 LSTM 的 RNN 对商用微波链路中的衰减进行短期预测

• DOI: 10.23919/eusipco47968.2020.9287835
• 发表时间: 2021
• 期刊: 2020 28th European Signal Processing Conference (EUSIPCO
• 作者: Jacoby, Dror;Ostrometzky, Jonatan;Messer, Hagit
• 通讯作者: Messer, Hagit

Adaptive Fuzzy-Based Models for Attenuation Time Series Forecasting

用于衰减时间序列预测的自适应模糊模型

• 发表时间: 2022
• 期刊: 2021
• 作者: D. Jacoby, J. Ostrometzky

COSMOS educational toolkit: using experimental wireless networking to enhance middle/high school STEM education

COSMOS 教育工具包：使用实验性无线网络增强初中/高中 STEM 教育

• DOI: 10.1145/3431832.3431839
• 发表时间: 2020
• 期刊: ACM SIGCOMM Computer Communication Review
• 影响因子: 2.8
• 作者: Skrimponis, Panagiotis;Makris, Nikos;Rajguru, Sheila Borges;Cheng, Karen;Ostrometzky, Jonatan;Ford, Emily;Kostic, Zoran;Zussman, Gil;Korakis, Thanasis
• 通讯作者: Korakis, Thanasis