Green UAV communications for 6G: A survey

Wireless power transfer (WPT) is a promising charging technology for battery-limited sensors. In this paper, we study the use of an unmanned aerial vehicle (UAV) as a charger for WPT. Unlike the previous works, our study takes into account the power consumption of the UAV (power consumption during hovering and flight), the charging process from a base station (BS) to the UAV and the conversion loss of the energy harvester. Both one-dimensional (1D) and two-dimensional (2D) WPT systems are considered. The sum-energy received by all sensors is maximized to find the optimal strategy for UAV deployment. Two different charging schemes are proposed. Numerical results show that the sum-energy received by all sensors is determined by sensors’ topology, the flight speed of the UAV and the transmit power. They also show that, when the BS charging process and the UAV power consumption are considered in the optimization, the optimal location of the UAV in the 1D and 2D WPT systems is closer to the BS than in the previous works that ignore these two practical factors.

/pdf/uav-enabled-wireless-power-transfer-with-base-station-1tkbu9l4t2.pdf

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

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) 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 NOMA-based 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.

/pdf/noma-based-uav-aided-networks-for-emergency-communications-578ipzccsr.pdf

NOMA-based UAV-aided networks for emergency communications

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV’s energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices’ data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives.

/pdf/multi-objective-optimization-for-uav-assisted-wireless-1a1x3ir7ao.pdf

Multi-Objective Optimization for UAV-Assisted Wireless Powered IoT Networks Based on Extended DDPG Algorithm

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.

/pdf/joint-3d-trajectory-design-and-time-allocation-for-uav-2ftf0vlw9n.pdf

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

This paper considers an unmanned aerial vehicle (UAV)-aided millimeter Wave (mmWave) multiple-input-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 three-dimensional (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.

/pdf/joint-3d-trajectory-and-power-optimization-for-uav-aided-3mkaxqu3u2.pdf

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

In this paper, a UAV-assisted wireless powered communication system for IoT network is studied. Specifically, the UAV performs as base station (BS) to collect the sensory information of the IoT devices as well as to broadcast energy signals to charge them. Considering the devices' limited data storage capacity and battery life, we propose a multi-objective optimization problem that aims to minimize the average data buffer length, maximize the residual battery level of the system and avoid data overflow and running out of battery of devices. Since the services requirements of IoT devices are dynamic and uncertain and the system can not be full observed by the UAV, it is challenging for UAV to achieve trajectory planning. In this regard, a deep Q network (DQN) is applied for UAV's flight control. Simulation results indicate that the DQN-based algorithm provides an efficient UAV's flight control policy for the proposed optimization problem.

/pdf/trajectory-planning-of-uav-in-wireless-powered-iot-system-2cvuwwykfg.pdf

Trajectory Planning of UAV in Wireless Powered IoT System Based on Deep Reinforcement Learning

Joint 3D placement and multi-beam design for UAV-assisted wireless power transfer networks

Disclosed in the present invention is an unmanned aerial vehicle three-dimensional trajectory design method based on a wireless energy transmission network. The method comprises: establishment of a downlink channel model of a wireless energy transmission network; establishment of a mathematical model based on maximization of energy harvested by a user, comprising three-dimensional position deployment, charging time allocation, and energy beam pattern generation of an unmanned aerial vehicle; design and analysis of a low-complexity iterative algorithm for jointly optimizing the three-dimensional position deployment, charging time, and energy beam of the unmanned aerial vehicle; and design of an unmanned aerial vehicle three-dimensional flight trajectory by a branch and bound method. The present invention proposes to apply three-dimensional energy beamforming technology to a wireless energy transmission network, establish a mathematical optimization problem, of the network model, based on the maximization of energy harvested by a user, and apply a low-complexity iterative algorithm to jointly optimize the three-dimensional position deployment, charging time, and energy beam pattern of an unmanned aerial vehicle, so as to maximize the energy harvested by the user in a coverage area while meeting altitude and coverage area restrictions of the unmanned aerial vehicle.

Shaopeng Ao

Papers

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

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

Trajectory Planning of UAV in Wireless Powered IoT System Based on Deep Reinforcement Learning

Joint 3D placement and multi-beam design for UAV-assisted wireless power transfer networks

Unmanned aerial vehicle three-dimensional trajectory design method based on wireless energy transmission network