Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks

Abstract

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations (BSs) 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 UAVs' 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 non-convex 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.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

“…In a number of papers, each user receives particular time slots to exchange information with the drone (so‐called time‐division multiple access (TDMA)). These papers consider scheduling a drone's communication with ground mobile devices and optimizing the trajectory of the drone . Jeong et al schedule data computation by a drone serving as a cloudlet as well as data transmission between the cloudlet and a mobile device to minimize the total energy expenditure of the drone given its fixed trajectory.…”

Joint Trajectory and Resource Allocation Design for Energy-Efficient Secure UAV Communication Systems

“…Thus, we handle the data rate in the objective function and constraint C6 since it is the difference of convex (DC) functions. Based on the SCA and [32], [43], for given feasible points (α I k,i [n]) j A1 and (Z J k,i [n]) j A1 , a lower bound of the data rate can be obtained by its first-order Taylor expansion as…”

“…(58) [15], [27], [31], [25]. solution converges to a suboptimal optimal solution [37], [50], [32], [51], [52] of the original problem in (22).…”

“…Impact of Jammer UAV's Trajectory Figure 6 shows the corresponding information UAV's trajectories for different predetermined trajectories of the jammer UAV with T = 50 s. In this paper, we consider six commonly adopted trajectories of the jammer UAV, which have the same flight velocity 12 at 10.4 m/s, with the following proposed scheme. (a) Center of the service area (CSA), in which the jammer UAV adopts a circular trajectory centered at the center of the service area [250, 250] with a radius of 150 meters [27]; (b) Center of the eavesdroppers area (CEA), where the jammer UAV patrols also with a circular trajectory but centered at [312.5, 187.5] (centroid of all estimated eavesdroppers' locations) with a radius of 159 meters [32]; (c) Shuttling flight between the eavesdroppers (SFE), where the jammer UAV flight is shuttled back and forth between the estimated locations of the two eavesdroppers during the given 12 Note that a UAV consumes the minimum flight power when it travels at 10.4 m/s for the considered setting in [31]. time frame; (d) Centered at [400, 100] (CA1), (e) Centered at [375,175] (CA2), and (f) Centered at [250,250] (CA3), in which the jammer UAV has a circular trajectory with a radius of 10 meters centered at the eavesdropper 1's estimated location [400, 100], the middle of two eavesdroppers' estimated locations [375,175], and the eavesdropper 2's estimated location [250,250], respectively.…”

Probabilistic Caching for Small-Cell Networks With Terrestrial and Aerial Users

“…We assume that the SBSs, the TUs and the UAVs are deployed according to three independent HPPPs with the height of h BS , h TU and h AU , respectively. With reference to [5] and [24], consider that all the UAVs are positioned at the same height 1 . Let λ s be the density of SBSs, λ TU be the density of TUs, and λ AU be the density of UAVs.…”