Beyond Empirical Models: Pattern Formation Driven Placement of UAV Base Stations - 2018


This Project considers the location of unmanned aerial vehicle base stations (UAV-BSs) with criterion of minimum UAV-recall-frequency (UAV-RF), indicating the energy potency of mobile UAVs networks. Many different power consumptions, including signal transmit power, on-board circuit power and the ability for UAVs mobility, and the ground user density are taken into consideration. Rather than typical empirical stochastic models, this Project utilizes a pattern formation system to track the instable and non-ergodic time-varying nature of user density. We show that for one time-slot, the optimal placement is achieved when the transmit power of UAV-BSs equals their on-board circuit power. Then, for multiple time-slot length, we tend to prove that the optimal placement updating downside is an integer nonlinear programming let alone an inherent integer linear programming. Since the original downside is NP-hard and can't be solved with conventional recursive strategies, we tend to propose a sequential-Markov-greedy-call strategy to realize close to minimal UAV-RF in polynomial time. Furthermore, we prove that the increment of UAV-RF caused by inaccurate predicted user density is proportional to the generalization error of learned patterns. Here, in regions with giant area, high-rise buildings, or low user density, giant sample sets are required for effective pattern formation.

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