Beyond Empirical Models: Pattern Formation Driven Placement of UAV Base Stations - 2018 PROJECT TITLE :Beyond Empirical Models: Pattern Formation Driven Placement of UAV Base Stations - 2018ABSTRACT: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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Basis Function Selection of Frequency-Domain Hammerstein Self-Interference Canceller for In-Band Full-Duplex Wireless Communications - 2018 Capacity and Delay Tradeoff of Secondary Cellular Networks With Spectrum Aggregation - 2018
