In this paper, the effective use of multiple quadrotor drones as an aerial antenna array that provides wireless service to ground users is investigated. In particular, under the goal of minimizing the airborne service time needed for communicating with ground users, a novel framework for deploying and operating a drone-based antenna array system whose elements are single-antenna drones is proposed. In the considered model, the service time is minimized by jointly optimizing the wireless transmission time as well as the control time that is needed for movement and stabilization of the drones. To minimize the transmission time, first, the antenna array gain is maximized by optimizing the drone spacing within the array. In this case, using perturbation techniques, the drone spacing optimization problem is addressed by solving successive, perturbed convex optimization problems. Then, the optimal locations of the drones around the array's center are derived such that the transmission time for the user is minimized. Given the determined optimal locations of drones, the drones must spend a control time to adjust their positions dynamically so as to serve multiple users. To minimize this control time of the quadrotor drones, the speed of rotors is optimally adjusted based on both the destinations of the drones and external forces (e.g., wind and gravity). In particular, using bang-bang control theory, the optimal rotors' speeds as well as the minimum control time are derived in closed-form. Simulation results show that the proposed approach can significantly reduce the service time to ground users compared to a multi-drone scenario in which the same number of drones are deployed separately to serve ground users. The results also show that, in comparison with the multi-drones case, the network's spectral efficiency can be significantly improved.
  This paper presents a design of UAVs-based 3D antenna arrays for a maximum performance in terms of directivity and side lobe level (SLL). This paper illustrates how to model the UAVs formation flight using 3D nonuniform antenna arrays. This design of 3D antenna arrays considers the optimization of the positions of the antenna elements to model the UAVs formation flight. In this case, a disk patch antenna is chosen to be used as element in each UAV. The disk patch antenna is formulated by the well-known cavity model. The synthesis process is carried out by the method of Differential Evolution for Multiobjective Optimization (DEMO). Furthermore, a comparison of the performance of 3D nonuniform antenna arrays is provided with respect to the most conventional arrays (circular, planar, linear, and the cubic) for UAVs formation flight. This paper is concerned with providing radio access network (RAN) elements (supply) for flash crowd traffic demands. The concept of multi-tier cells [heterogeneous networks (HetNets)] has been introduced in 5G network proposals to alleviate the erratic supply–demand mismatch. However, since the locations of the RAN elements are determined mainly based on the long-term traffic behavior in 5G networks, even the HetNet architecture will have difficulty in coping up with the cell overload induced by flash crowd traffic. In this paper, we propose a proactive drone-cell deployment framework to alleviate overload conditions caused by flash crowd traffic in 5G networks. First, a hybrid distribution and three kinds of flash crowd traffic are developed in this framework. Second, we propose a prediction scheme and an operation control scheme to solve the deployment problem of drone cells according to the information collected from the sensor network. Third, the software-defined networking technology is employed to seamlessly integrate and disintegrate drone cells by reconfiguring the network. Our experimental results have shown that the proposed framework can effectively address the overload caused by flash crowd traffic.  The modern battlefield scenario presents a number of challenges that highlights the importance of data gathering and information flow as crucial to the success of a military operation. In this context, network-centric warfare systems play an important role to bridge the gap between data and information sources and consumers. These systems are generally composed of different types of networks both deployed on the field and on back-end facilities. A paramount problem faced when conceiving the interconnection between the networked devices is how to provide robust and dependable connections to the devices on the field. This work tackles this issue proposing the use of the self-organizing paradigm to design efficient UAV relay networks to support military operations. As a proof of concept, realistic simulation experiments are performed providing clear indications of the claimed benefits of the self-organizing paradigm applied to these military systems.  In this letter, the efficient deployment of multiple unmanned aerial vehicles (UAVs) acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the 3-D locations of the UAVs is determined in a way that the total coverage area is maximized while maximizing the coverage lifetime of the UAVs. Our results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements. Furthermore, the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area is determined. Numerical results evaluate various tradeoffs. In this paper, the capacity of airborne multiple-input-multiple-output (MIMO) wireless communication systems with arbitrary alignments of linear transmit and receive antenna arrays is systematically analyzed and the maximum achievable capacity is determined. Based on a general three-dimensional (3D) airborne MIMO communication model, we are able to approximate the airborne MIMO capacity as a function of the transmit and receive antenna array geometry in the 3D space. The capacity approximation is asymptotically tight as the distance between the transmit and receive antenna arrays large compared to their size. Based on the asymptotically tight capacity approximation, we derive an upper bound as well as a lower bound of the airborne MIMO capacity. Interestingly, both the upper and lower bounds are achievable. We also derive a necessary and sufficient condition for airborne MIMO communication systems to achieve the capacity upper bound for any given 3D transceiver antenna array geometry. The necessary and sufficient condition allows us to properly select the system parameters and design airborne MIMO communication systems that reach the best possible performance in terms of system capacity. We prove that when the distance between the transmit and receive antenna arrays is within a certain range, there exists a set of system parameter values (e.g. antenna element separation) for which the capacity of the MIMO communication system achieves the theoretical upper bound and this capacity value is larger than the average capacity of the corresponding conventional MIMO communication system under Rayleigh fading. Finally, we prove that the airborne MIMO capacity converges to the capacity lower bound when the distance between the transmit and receive antenna arrays goes to infinity. Extensive numerical studies included in this paper illustrate and validate our theoretical developments. Due to the high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAV’s trajectory and power control. The formulated problem is a mixed integer nonconvex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the nonconvex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.  This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a number of geographically distributed ground terminals (GTs). Our objective is to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT is able to successfully recover the file with a high probability required. We consider the use of practical random linear network coding (RLNC) for UAV multicasting, so that each GT is able to recover the file as long as it receives a sufficiently large number of coded packets. However, the formulated UAV trajectory optimization problem is non-convex and difficult to be directly solved. To tackle this issue, we first derive an analytical lower bound for the success probability of each GT's file recovery. Based on this result, we then reformulate the problem into a more tractable form, where the UAV trajectory only needs to be designed to meet a set of constraints each on the minimum connection time with a GT, during which their distance is below a designed threshold. We show that the optimal UAV trajectory only needs to constitute connected line segments, thus it can be obtained by determining first the optimal set of waypoints and then UAV speed along the lines connecting the waypoints. We propose practical schemes for the waypoints design based on a novel concept of virtual base station (VBS) placement and by applying convex optimization techniques. Furthermore, for given set of waypoints, we obtain the optimal UAV speed over the resulting path efficiently by solving a linear programming (LP) problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains as compared to benchmark schemes.  In this paper, the effective use of flight-time constrained unmanned aerial vehicles (UAVs) as flying base stations that provide wireless service to ground users is investigated. In particular, a novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as the UAVs’ hover duration (i.e. flight time) is proposed. In the considered model, UAVs hover over a given geographical area to serve ground users that are distributed within the area based on an arbitrary spatial distribution function. In this case, two practical scenarios are considered. In the first scenario, based on the maximum possible hover times of UAVs, the average data service delivered to the users under a fair resource allocation scheme is maximized by finding the optimal cell partitions associated to the UAVs. Using the powerful mathematical framework of optimal transport theory, this cell partitioning problem is proved to be equivalent to a convex optimization problem. Subsequently, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users’ distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirements of ground users, the minimum average hover time that the UAVs need for completely servicing their ground users is derived. To this end, first, an optimal bandwidth allocation scheme for serving the users is proposed. Then, given this optimal bandwidth allocation, optimal cell partitions associated with the UAVs are derived by exploiting the optimal transport theory. Simulation results show that our proposed cell partitioning approach leads to a significantly higher fairness among the users compared with the classical weighted Voronoi diagram. Furthermore, the results demonstrate that the average hover time of the UAVs can be reduced by 64 by adopting the proposed optimal bandwidth allocation scheme as well as the optimal cell partitioning approach. In addition, our results reveal an inherent tradeoff between the hover time of UAVs and bandwidth efficiency while serving the ground users. 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. 