With recent advancements in drone technology, researchers are now considering the possibility of deploying small cells served by base stations mounted on flying drones. A major advantage of such drone small cells is that the operators can quickly provide cellular services in areas of urgent demand without having to pre-install any infrastructure. Since the base station is attached to the drone, technically it is feasible for the base station to dynamic reposition itself in response to the changing locations of users for reducing the communication distance, decreasing the probability of signal blocking, and ultimately increasing the spectral efficiency. In this paper, we first propose distributed algorithms for autonomous control of drone movements, and then model and analyse the spectral efficiency performance of a drone small cell to shed new light on the fundamental benefits of dynamic repositioning. We show that, with dynamic repositioning, the spectral efficiency of drone small cells can be increased by nearly 100 for realistic drone speed, height, and user traffic model and without incurring any major increase in drone energy consumption.
Because of the flexibility and agility of drones, deploying the drone base stations (BSs) in optimal locations to maximize various network metrics are investigated in the literature. Al- @cite_17 provided an analytical model to find an optimal altitude for one UAV that provides the maximum coverage of the area. A service threshold in terms of maximum allowable path loss is defined in this model. Another recent study by @cite_25 studied the problem of finding the optimal cell boundaries and deployment location for multiple non-interfering UAVs. The objective of that study was to minimize the total transmission power of the UAVs.
@cite_25 @cite_17
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In this paper, the optimal deployment of multiple unmanned aerial vehicles (UAVs) acting as flying base stations is investigated. Considering the downlink scenario, the goal is to minimize the total required transmit power of UAVs while satisfying the users' rate requirements. To this end, the optimal locations of UAVs as well as the cell boundaries of their coverage areas are determined. To find those optimal parameters, the problem is divided into two sub-problems that are solved iteratively. In the first sub-problem, given the cell boundaries corresponding to each UAV, the optimal locations of the UAVs are derived using the facility location framework. In the second sub-problem, the locations of UAVs are assumed to be fixed, and the optimal cell boundaries are obtained using tools from optimal transport theory. The analytical results show that the total required transmit power is significantly reduced by determining the optimal coverage areas for UAVs. These results also show that, moving the UAVs based on users' distribution, and adjusting their altitudes can lead to a minimum power consumption. Finally, it is shown that the proposed deployment approach, can improve the system's power efficiency by a factor of 20 χ compared to the classical Voronoi cell association technique with fixed UAVs locations. Using drone base stations (drone-BSs) in wireless networks has started attracting attention. Drone-BSs can assist the ground BSs in both capacity and coverage enhancement. One of the important problems about integrating drone-BSs to cellular networks is the management of their placement to satisfy the dynamic system requirements. In this paper, we propose a method to find the positions of drone-BSs in an area with different user densities using a heuristic algorithm. The goal is to find the minimum number of drone-BSs and their 3D placement so that all the users are served. Our simulation results show that the proposed approach can satisfy the quality-of-service requirements of the network. The use of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is a promising approach to meet extreme data rate, coverage or power saving requirements in future wireless networks. However, the potential benefits that an ABS can provide depend strongly on its altitude, which affects the fading statistics of its transmissions. In this paper, we propose an altitude dependent performance model that allows to analyze both power and sum-rate capacity gain of the ABS. In contrast to the existing literature, we take into account the height-dependent small-scale fading and path loss exponent in order to provide a complete characterization of the air-to-ground communication links. Our results show that there is an optimal altitude range, representing a trade-off between sum-rate and transmit power gains. Within that optimal range, the height of the ABS can be adapted to optimize for rate or power respectively. The derivations are based on the outage probability of the network, which is studied as function of altitude as well. In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide the fly wireless communications to a given geographical area is analyzed. In particular, the coexistence between the UAV, that is transmitting data in the downlink, and an underlaid device-to-device (D2D) communication network is considered. For this model, a tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are considered: a static UAV and a mobile UAV. In the first scenario, the average coverage probability and the system sum-rate for the users in the area are derived as a function of the UAV altitude and the number of D2D users. In the second scenario, using the disk covering problem, the minimum number of stop points that the UAV needs to visit in order to completely cover the area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D users, the overall outage probability of the D2D users is derived. Simulation and analytical results show that, depending on the density of D2D users, the optimal values for the UAV altitude, which lead to the maximum system sum-rate and coverage probability, exist. Moreover, our results also show that, by enabling the UAV to intelligently move over the target area, the total required transmit power of UAV while covering the entire area, can be minimized. Finally, in order to provide full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.