Energy-limited devices and connectivity in complicated environments are two challenges for Internet of Things (IoT)-enabled mobile networks. Unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) is emerging as a promising technique to tackle the above problems, and thus provide cost-effective connectivity for IoT devices. In this paper, a unified framework of UAV-enabled SWIPT for IoT networks is established. First, three-dimensional (3D) trajectory and charging strategy of UAVs are investigated for maximizing the sum received power of energy receivers (ERs), where an antenna array provided by a UAV is used to generate multi-beams to charge the ERs simultaneously. Then, a trajectory planning scheme for multiple UAVs based on deep-Q-learning (DQL) is studied to maximize the minimum throughput of users by jointly optimizing the trajectory and resource scheduling of UAVs. In addition, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to achieve the correct prediction of the service requirement of devices (i.e., data transmission and battery charging) and, then, a dynamic path planning scheme is designed to maximize the energy efficiency (EE) of the system. Simulation results demonstrate the effectiveness of the above schemes. Finally, potential research directions and challenges are also discussed. Index Terms Deep reinforcement learning, Internet of Things (IoT), simultaneous wireless information and power transfer (SWIPT), trajectory optimization, unmanned aerial vehicle (UAV)

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.

High spectrum efficiency (SE) requirement and massive connections are the main challenges for the fifth generation (5G) and beyond 5G (B5G) wireless networks, especially for the case when Internet of Things (IoT) IoT devices are located in a disaster area. Non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-aided network is emerging as a promising technique to overcome the above challenges. In this paper, an emergency communications framework of NOMAbased UAV-aided networks is established, where the disasters scenarios can be divided into three broad categories that have named emergency areas, wide areas and dense areas. First, a UAV-enabled uplink NOMA system is established to gather information from IoT devices in emergency areas. Then, a joint UAV deployment and resource allocation scheme for a multi-UAV enabled NOMA system is developed to extend the UAV coverage for IoT devices in wide areas. Furthermore, a UAV equipped with an antenna array has been considered to provide wireless service for multiple devices that are densely distributed in disaster areas. Simulation results are provided to validate the effectiveness of the above three schemes. Finally, potential research directions and challenges are also highlighted and discussed. Index Terms-Emergency communications, non-orthogonal multiple access (NOMA), Internet of Things (IoT), trajectory optimization, unmanned aerial vehicle (UAV).

This paper considers an unmanned aerial vehicle (UAV)-aided millimeter Wave (mmWave) multipleinput-multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV serves as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices in different clusters. We aim to maximize the downlink sum rate by jointly optimizing the threedimensional (3D) placement of the UAV, beam pattern and transmit power. To address this problem, we first transform the non-convex problem into a total path loss minimization problem, and hence the optimal 3D placement of the UAV can be achieved via standard convex optimization techniques.Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is presented for the shaped-beam pattern synthesis of an antenna array. Finally, by transforming the original problem into an optimal power allocation problem under the fixed 3D placement of the UAV and beam pattern, we derive the closed-form expression of transmit power based on Karush-Kuhn-Tucker (KKT) conditions. In addition, inspired by fraction programming (FP), we propose a FP-based suboptimal algorithm to achieve a near-optimal performance. Numerical results demonstrate that the proposed algorithm achieves a significant performance gain in terms of sum rate for all IoT devices, as compared with orthogonal frequency division multiple access (OFDMA) scheme.

Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wirelesspowered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices.Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV's 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.