Internet of Things (IoT) and sensor fusion techniques may be used to collect data about the current positions and anticipated movements of wireless subscribers, thus enabling fifth-generation (5G) networks to proactively avoid overloads by modifying their capacities and coverage patterns. Data may be collected, for example, from mass transit systems, smart highway systems, smart buildings, security cameras, 5G networks themselves, and other sources, and machine learning techniques may be applied to generate maps of anticipated load patterns. As a result, the 5G networks may then be adjusted to address overloads in various manners, such as by adjusting the service bandwidth, by realigning antennas and multiple in, multiple out (MIMO) systems, by dispatching mobile cells, etc.
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Hanes, David; Byers, Chuck; Clarke, Joe; and Salgueiro, Gonzalo, "PREDICTING 5G USAGE AND RESOURCE CONTENTION THROUGH REAL-TIME MASS TRANSIT AND TRAFFIC MAPPING", Technical Disclosure Commons, (June 20, 2018)