NSF-AoF: CNS Core: Small: CRUISE: A Cross-system Architecture Design for Autonomous Wireless Networks based on Lifelong Machine Learning

NSF-AoF：CNS 核心：小型：CRUISE：基于终身机器学习的自主无线网络的跨系统架构设计

• 批准号: 2225427
• 负责人: Beatriz Lorenzo
• 金额: $ 28万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225427&HistoricalAwards=false
• 关键词: NSF; AoF; CNS; Core; Small

The next generation networking systems (6G and beyond) networks are expected to be massively connected and complex, able to fulfill the requirements of intelligent transportation, industrial automation, and augmented/virtual reality, among others. The highly dynamic nature of these services requires an advanced and autonomous network design that integrates communication, information, and AI which will enable much richer applications. The outcomes of the research will facilitate the integration of robust machine learning solutions with value-added services, thus fostering the creation of new digital ecosystems in other key sectors such as intelligent transportation systems, smart cities, and the Industrial Internet. The results will be disseminated to the scientific community as both individual and joint research publications of the PIs and their Finnish collaborators, and in tutorials, seminars, and workshops.This project aims to advance the design of next-generation networking systems (6G and beyond) by developing a cross-system architecture design methodology that enables the optimal integration of information, communication, and AI. This research will advance the state of the knowledge through the following activities: (i) Development of “Time-Aware Lifelong Learning” algorithms to address the dynamics in wireless networks. (ii) Modeling the tradeoff between complexity and accuracy of the algorithms to predict system parameters in real-time; (iii) Performing multitask learning for real-time resource management; (iv) determination of the level of connectivity reinforcement needed to compensate for the imperfections in the information to meet the high reliability and low latency requirements; (v) development of the methodology to perform the tasks above when processes are temporal in nature.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.

下一代网络系统(6G及以上)网络预计将是大规模连接和复杂的，能够满足智能交通、工业自动化和增强/虚拟现实等方面的要求。这些服务的高度动态特性需要先进的自主网络设计，将通信、信息和人工智能集成在一起，从而实现更丰富的应用。研究成果将促进稳健的机器学习解决方案与增值服务的整合，从而促进在智能交通系统、智慧城市和工业互联网等其他关键领域创建新的数字生态系统。研究结果将作为私人投资机构及其芬兰合作者的个人和联合研究出版物，以及通过教程、研讨会和研讨会向科学界传播。该项目旨在通过开发能够实现信息、通信和人工智能最佳整合的跨系统架构设计方法，推动下一代网络系统(6G及以后)的设计。这项研究将通过以下活动促进知识的状态：(I)开发“时间感知的终身学习”算法来解决无线网络中的动态问题。(Ii)建立算法的复杂性和准确性之间的折衷模型，以实时预测系统参数；(Iii)为实时资源管理执行多任务学习；(Iv)确定为补偿信息中的缺陷以满足高可靠性和低延迟要求所需的连通性加固水平；(V)开发在过程具有时间性的情况下执行上述任务的方法。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。