Forecasting Cellular Connectivity for Cyber-Physical Systems: A Machine Learning Approach

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.