Abstract
The technologies vested by the introduction of fifth generation (5G) networks as well as the emerging 6G systems present opportunities for enhanced communication and computational capabilities that will advance many large-scale critical applications in the critical domains of manufacturing, extended reality, power generation and distribution, water, agriculture, transportation, healthcare, and defense and security, among many others. However, for these enhanced communication networks to take full effect, these networks, including wireless infrastructure, end-devices, edge/cloud servers, base stations, core network and satellite-based elements, should be equipped with real-time decision support capabilities, cognizant of multilevel and multimodal time-varying conditions, to enable self-sustainment of the networks and communications infrastructures, for optimal management and adaptive resource allocation with minimum possible intervention from operators. To meet the highly dynamic and extreme performance requirements of these heterogeneous multi-component, multilayer communication infrastructures on latency, data rate, reliability, and other user-defined metrics, these support methods will need to leverage the accuracy of full-scale models for multi-objective optimization, adaptive management, and control of time-varying and complex operations. This paper discusses how algorithmic, methodological, and instrumentation capabilities learned from Dynamic Data Driven Applications Systems (DDDAS)-based methodologies can be applied to enable optimized and resilient design and operational management of the complex and highly dynamic 5G/6G communication infrastructures. Such smart DDDAS capabilities are unswervingly proven for more than two decades on adaptive real-time control of various systems requiring the high accuracy of full-scale modeling for multi-objective real-time decision making with efficient computational resource utilization.
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Acknowledgements
This study was supported by the Air Force Office of Scientific Research (AFOSR) Award No: FA9550-19-1-0383.
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Celik, N., Darema, F., Runsewe, T., Saad, W., Yavuz, A. (2024). DDDAS for Optimized Design and Management of 5G and Beyond 5G (6G) Networks. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_12
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