This paper considers the exploitation of unmanned aerial vehicles (UAVs) in wireless networking, with which communication-enabled robots operate as flying wireless relays to provide connectivity or a capacity boost to a ground user. We focus on the particular problem of (automatic) UAV positioning, which greatly affects the end-to-end throughput performance. While existing methods rely on propagation distance minimiza- tion and statistical models for the presence or absence of a line-of-sight (LOS), we propose an approach capable of leveraging local topological information so as to offer better performance guarantees. The proposed method allows to strike a trade-off between minimizing distance path loss and discovering (near) LOS opportunities at locations away from the base station (BS)-user axis. Furthermore, the algorithm is shown to find the global optimal UAV position, although it only requires a local exploration of a signal strength map and the length of search trajectory is only linear to the geographical scale. Hence, it lends itself to online implementation. Significant throughput gains are found when compared to other positioning approaches based on LOS statistical models.
One essential issue for positioning the UAV is to predict the air-to-ground channel, since a precise UAV-user channel knowledge is usually not available before the UAV is sent to a target position. As a way to circumvent this problem, some prior works simply assumed LOS propagation from the UAV to the ground user, and hence the channel gain is merely an explicit continuous function of the UAV position. Using the LOS model, @cite_25 @cite_19 @cite_24 @cite_14 focused on UAV navigation problems, and @cite_5 studied the mimo channel to jointly optimize the UAV position and the orientation of a ula mounted at the UAV for the best spatial channel to multiple users. However, LOS models overestimate the channel gain in urban scenarios, especially in low altitude UAV applications, because there the air-to-ground signal is likely blocked by obstacles surrounding the user, and such a user-side shadowing effect significantly dominates the relay performance.
@cite_14 @cite_24 @cite_19 @cite_5 @cite_25
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We consider a collection of single-antenna ground nodes communicating with a multi-antenna unmanned aerial vehicle (UAV) over a multiple-access ground-to-air communications link. The UAV uses beamforming to mitigate inter-user interference and achieve spatial division multiple access (SDMA). First, we consider a simple scenario with two static ground nodes and analytically investigate the effect of the UAV's heading on the system sum rate. We then study a more general setting with multiple mobile ground-based terminals, and develop an algorithm for dynamically adjusting the UAV heading to maximize the approximate ergodic sum rate of the uplink channel, using a prediction filter to track the positions of the mobile ground nodes. For the common scenario where a strong line-of-sight (LOS) channel exists between the ground nodes and UAV, we use an asymptotic analysis to find simplified versions of the algorithm for low and high SNR. We present simulation results that demonstrate the benefits of adapting the UAV heading in order to optimize the uplink communications performance. The simulation results also show that the simplified algorithms provide near-optimal performance. In this paper, we study coverage probabilities of the UAV-assisted cellular network modeled by 2-dimension (2D) Poisson point process. The cellular user is assumed to connect to the nearest aerial base station. We derive the explicit expressions for the downlink coverage probability for the Rayleigh fading channel. Furthermore, we explore the behavior of performance when taking the property of air-to-ground channel into consideration. Our analytical and numerical results show that the coverage probability is affected by UAV height, pathloss exponent and UAV density. To maximize the coverage probability, the optimal height and density of UAVs are studied, which could be beneficial for the UAV deployment design. 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. 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.