Unmanned aerial vehicle (UAV) can effectively work as temporary base station or access point in the air to transfer/receive data to/from ground terminals (GTs). However, UAV-GT links might be blocked by ground obstacles, like buildings in urban area, leading to a poor performance on data transferring rate. To address this problem, reconfigurable intelligent surface (RIS), as a promising technique, can intelligently reflect the received signals between UAV and GT to significantly enhance the communication quality. Under this deployment of RIS-assisted UAV, we intend to jointly optimize the 3D-space of the UAV and the phase-shift of the RIS to maximize the data transferring rate of the UAV, while minimizing the UAV propulsion energy. The joint problem is non-convex in its original form and difficult to be timely solved by using traditional method, like successive convex approximation (SCA). Therefore, to facilitate the online decision making to this joint problem, we leverage deep reinforcement learning (DRL) to learn the near-optimal solution, and the well known Double Deep Q-Network (DDQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are ultilized. Numerical results show that DRL can effectively improve the energy-efficiency performance of the RIS-Assisted UAV system, compared with benchmark solutions.

Unmanned aerial vehicle (UAV) can work as mobile edge computing (MEC) server in the sky to provide communication and computation services to ground terminals (GTs), due to its high mobility. However, UAV might work in complicated environment, where UAV-GT links being frequently blocked by ground obstacles, then leading to a poor quality of service (QoS) on task latency. To this problem, reconfigurable intelligent surface (RIS) can assist UAV and improve the wireless environment by reflecting the transmitted signals between the UAV and GTs. RIS-assisted UAV thus can have a greatly improved performance on mobile computing. Under this deployment, to maximize the energy-efficiency of the RIS-assisted UAV, this letter tries to study the joint optimization of UAV trajectory, task offloading and cache with the phase-shift design of the RIS. We intent to ultilize the successive convex approximation (SCA) method to solve the joint problem, which is non-convex in its original form. Numerical results show that the joint optimization can improve the performance of the RIS-assisted UAV, compared with the benchmark solutions.

This article studies an unmanned aerial vehicle (UAV)-enabled edge-cloud system, where UAV acts as a mobile edge computing (MEC) server interplaying with remote central cloud to provide computation services to ground terminals (GTs). The UAV-enabled edge-cloud system implements a virtualized network function, namely, mobile clone (MC), for each GT to help execute their offloaded tasks. Through such network function virtualization (NFV) implemented on top of the UAV-enabled edge-cloud system, GTs can have extended computation capability and prolonged battery lifetime. We aim to jointly optimize the allocation of resource and the UAV trajectory in the 3-D spaces to minimize the overall energy consumption of the UAV. The proposed solution, therefore, can extend the endurance of the UAV and support reliable MC functions for GTs. This article solves the complicated optimization problem through a block coordinate descent algorithm in an iterative way. In each iteration, the allocation of resource is modeled as a multiple constrained optimization problem given predefined UAV trajectory, which can be reformulated into a more tractable convex form and solved by successive convex optimization and Lagrange duality. Second, given the allocated resource, the optimization of the trajectory of rotary-wing/fixed-wing UAV can be formulated into a series of convex quadratically constrained quadratically program (QCQP) problems and solved by the standard convex optimization techniques. After the block coordinate descent algorithm converges to a prescribed accuracy, a high-quality suboptimal solution can be found. According to the simulation, the numerical results verify the effectiveness of our proposed solution in contrast to the baseline solutions.

This letter considers a solar-powered unmanned aerial vehicle (UAV) system, where the UAV collects data from Internet of Things Devices (IoTDs) on the ground while also charging them using laser charging technology. Both the linear and nonlinear laser charging models are considered. We aim to maximize the residual energy of the UAV while satisfying the requirements of IoTDs by jointly optimizing the three-dimensional (3D) trajectory of UAV and scheduling for IoTDs. The optimization problem is formulated as a mixed integer non-convex problem which is difficult to solve in general. We apply the successive convex approximation and block coordinate descent algorithm to address this problem successfully. Simulation results verify the effectiveness of our algorithm compared with two benchmark methods.