Placement of 5G drone base stations by data field clustering
Iellamo, Stefano; Lehtomäki, Janne J.; Khan, Zaheer (2017-11-16)
S. Iellamo, J. J. Lehtomaki and Z. Khan, "Placement of 5G Drone Base Stations by Data Field Clustering," 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, 2017, pp. 1-5. doi: 10.1109/VTCSpring.2017.8108590
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We consider the problem of complementing the capacity of an existing network of macro base stations by dynamically placing a network of 5G small base stations in the form of Unnamed Aerial Vehicles UAV (better known as drones). Our goal is to maximize the capacity boost provided by the UAVs in each considered time frame and extend the battery life of the served mobile users. With this in mind, we propose two clustering algorithms that build on mobile users’ spatio-temporal data excess demand (here intended as the portion of demand which is not satisfactory addressed by the existing macro base stations). For the numerical analysis, we use real Beijing downtown trajectory data. The obtained results show that our algorithms perform well and can be considered for enabling real time connection provisioning.
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