Abstract
Many applications in the context of Cyber-Physical Systems (CPS) can be served by cellular communication systems. The additional data traffic has to be transmitted very efficiently to minimize the interdependence with Human-to-Human (H2H) communication. In this paper, a forecasting approach for cellular connectivity based machine learning methods to enable a resource-efficient communication for CPS is presented. The results based on massive measurement data show that the cellular connectivity can be predicted with a probability of up to 78 %. Regarding a mobile communication system, a predictive channel-aware transmission based on machine learning methods enables a gain of 33 % concerning the spectral resource utilization of an Long Term Evolution (LTE) system.
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Ide, C., Nick, M., Kaulbars, D., Wietfeld, C. (2016). Forecasting Cellular Connectivity for Cyber-Physical Systems: A Machine Learning Approach. In: Niggemann, O., Beyerer, J. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48838-6_3
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DOI: https://doi.org/10.1007/978-3-662-48838-6_3
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