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.
UAVs were also expected to establish emergency communication links during disaster situation and thus improve public safety in @cite_27 . That work showed that by optimal placement of UAVs the system throughput can be improved significantly. Brute force search was used to find the optimal location of UAVs in the target area.
@cite_27
1540107614 2285553406 2521710942 2783122018
Communications play an important role during public safety operations. Since the current communication technologies heavily rely on the backbone network, the failure of base stations (BSs) due to natural disasters or malevolent attacks causes communication difficulties for public safety and emergency communications. Recently, the use of unmanned aerial vehicles (UAVs) such as quadcopters and unmanned gliders have gained attention in public safety communications. They can be used as unmanned aerial base stations (UABSs), which can be deployed rapidly as a part of the heterogeneous network architecture. However, due to their mobile characteristics, interference management in the network becomes very challenging. In this paper, we explore the use of UABSs for public safety communications during natural disasters, where part of the communication infrastructure becomes damaged and dysfunctional (e.g., as in the aftermath of the 2011 earthquake and tsunami in Japan). Through simulations, we analyze the throughput gains that can be obtained by exploiting the mobility feature of the UAVs. Our simulation results show that when there is loss of network infrastructure, the deployment of UABSs at optimized locations can improve the throughput coverage and the 5th percentile spectral efficiency of the network. Furthermore, the improvement is observed to be more significant with higher path-loss exponents. Recently, the UAS has been extensively exploited for data collection from remote and dangerous or inaccessible areas. While most of its existing applications have been directed toward surveillance and monitoring tasks, the UAS can play a significant role as a communication network facilitator. For example, the UAS may effectively extend communication capability to disaster-affected people (who have lost cellular and Internet communication infrastructures on the ground) by quickly constructing a communication relay system among a number of UAVs. However, the distance between the centers of trajectories of two neighboring UAVs, referred to as IUD, plays an important role in the communication delay and throughput. For instance, the communication delay increases rapidly while the throughput is degraded when the IUD increases. In order to address this issue, in this article, we propose a simple but effective dynamic trajectory control algorithm for UAVs. Our proposed algorithm considers that UAVs with queue occupancy above a threshold are experiencing congestion resulting in communication delay. To alleviate the congestion at UAVs, our proposal adjusts their center coordinates and also, if needed, the radius of their trajectory. The performance of our proposal is evaluated through computer-based simulations. In addition, we conduct several field experiments in order to verify the effectiveness of UAV-aided networks. In this letter, an optimal placement and distribution of cooperative unmanned aerial vehicles (UAVs) in the next generation heterogeneous networks is presented in order to optimize the overall network delays. The positioning issue is formulated as a minimax facility problem which is then resolved using the entropy nets by formation of an optimal placement algorithm. The effectiveness of the proposed approach is demonstrated in terms of significant gains including increased UAV positioning accuracy and reduced delays in UAV allocation. Unmanned Aerial Vehicle (UAV) networks have emerged as a promising technique to rapidly provide wireless coverage to a geographical area out of the reach or capacity of existing core networks, where a flying UAV can be fast deployed to serve as a base station. Existing work on UAV overlook the emergency deployment problem and only the recent research on sensor networks study the deployment problems the assumed in one-dimensional (1D) ground. However, UAVs should be deployed to the air (beyond one-dimension) by considering their different flying speeds during deployment and deployment altitudes, this paper studies this novel emergency UAV deployment to minimize the UAV deployment delay till covering the whole target area. When a number @math of diverse UAVs are dispatched from the same location (e.g., the closest UAV station) to the target area, we present an optimal deployment algorithm by balancing UAVs' diverse flying speeds and coverage radii, and this algorithm has low computation complexity @math . When UAVs are generally dispatched from different locations, we first prove that the emergency UAV deployment problem is NP-complete. By preserving UAVs' location order, we then successfully design a fully polynomial time approximation scheme (FPTAS) of computation complexity @math to arbitrarily approach the global optimum.