Joint 3D Trajectory Design and Time Allocation for UAV-Enabled Wireless Power Transfer Networks

Abstract

This paper considers a rotary-wing unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV is dispatched as an energy transmitter (ET), transferring radio frequency (RF) signals to a set of energy receivers (ERs) periodically. We aim to maximize the energy harvested at all ERs by jointly optimizing the UAV's three-dimensional (3D) placement, beam pattern and charging time. However, the considered optimization problem taking into account the drone flight altitude and the wireless coverage performance is formulated as a non-convex problem. To tackle this problem, we propose a low-complexity iterative algorithm to decompose the original problem into four sub-problems in order to optimize the variables sequentially. In particular, we first use the sequential unconstrained convex minimization based algorithm to find the globally optimal UAV two-dimensional (2D) position. Subsequently, we can directly obtain the optimal UAV altitude as the objective function of problem is monotonic decreasing with respect to UAV altitude. Then, we propose the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm to control the phase of antenna array elements, in order to achieve high steering performance of multi-beams. Finally, with the above solved variables, the original problem is reformulated as a single-variable optimization problem where charging time is the optimization variable, and can be solved using the standard convex optimization techniques. Furthermore, we use the branch and bound method to design the UAV trajectory which can be constructed as traveling salesman problem (TSP) to minimize flight distance. Numerical results validate the theoretical findings and demonstrate that significant performance gain in terms of sum received power of ERs can be achieved by the proposed algorithm in UAV-enabled wireless power transfer networks.Index Terms-Multi-beam, trajectory optimization, UAV 3D placement, wireless power transfer.

Joint 3D Trajectory and Power Optimization for UAV-Aided mmWave MIMO-NOMA Networks

“…We denote the 2D locations of the UAV by z u = (x u , y u ), and its altitude as h. The location of the device k ∈{1, 2, • • • K} on the ground are z k = (x k , y k ). Since UAVs can be flexibly deployed and moved in 3D place, we assume that the UAV-ground channel is line-of-sight (LoS)-dominated, and the channel h k between the UAV and device k is given by [22], [23]…”

UAV-Enabled Wireless Power Transfer With Base Station Charging and UAV Power Consumption

“…11 shows the optimal location that maximizes the sum-energy, and compares the energy received by individual sensors when the UAV hovering at the optimal location in Scheme 1 of 2D case. In this simulation, ten sensors with (1003, 13), (1008, 31), (1016, 11), (1018, 33), (1020, 23), (1022, 18), (1029, 19), (1055, 29), (1060, 27), (1065, 38) as 2D coordinates are used and we set V = 9 m/s. As seen from Fig.…”

“…In [18], the maximum network throughput was discussed in a UAV-enabled relaying system where the UAV receives both energy and information from a BS, and then forwards the information to the ground user. The authors in [19] studied the use of a rotary-wing UAV as an energy transmitter to charge a set of energy receivers (ERs) taking into account the UAV's flight altitude and coverage performance. The energy harvested by all ERs was maximized via jointly optimizing the UAV's placement, beam pattern and charging time.…”

Robust 3D-Trajectory and Time Switching Optimization for Dual-UAV-Enabled Secure Communications

“…A max-min average rate optimization problem for an energy-constrained UAV-assisted downlink cellular network was studied in [29], in which a joint design of resource allocation and 3D trajectory was proposed. In [30], the authors considered a rotary-wing UAV-enabled wireless power transfer system, where the harvested energy at all energy receivers was maximized by jointly optimizing the UAV's 3D trajectory, beam pattern and charging time. Furthermore, in [31], the authors investigated a UAV-assisted cognitive communication network, where the average rate of the secondary receivers was maximized by jointly optimizing the UAV's 3D trajectory and power allocation.…”