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

下一代网络系统（6 G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/虚拟现实等的要求。这些服务的高度动态性需要先进的自主网络设计，集成通信，信息和人工智能，从而实现更丰富的应用程序。 研究成果将促进强大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。研究成果将通过PI及其芬兰合作者的个人和联合研究出版物以及教程、研讨会和讲习班向科学界传播。该项目旨在通过开发跨系统架构设计方法来推进下一代网络系统（6 G及以后）的设计，从而实现信息、通信和AI的最佳集成。这项研究将通过以下活动促进知识的发展：（一）开发“时间意识终身学习”算法，以解决无线网络中的动态问题。(ii)对算法的复杂性和准确性之间的权衡进行建模，以实时预测系统参数;（iii）执行实时资源管理的多任务学习;（iv）确定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接增强水平;（v）当过程是暂时性的时，开发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。

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.