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

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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.