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Due to their promising applications and intriguing characteristics, Unmanned Aerial Vehicles (UAVs) can be dispatched as flying base stations to serve multiple energy-constrained Internet-of-Things (IoT) sensors. Moreover, to ensure fresh data collection while providing sustainable energy support to a large set of IoT devices, a required number of UAVs should be deployed to carry out these two tasks efficiently and promptly. Indeed, the data collection requires that UAVs first make Wireless Energy Transfer (WET) to supply IoT devices with the necessary energy in the downlink. Then, IoT devices perform Wireless Information Transmission (WIT) to UAVs in the uplink based on the harvested energy. However, it turns out that when the same UAV performs WIT and WET, its energy usage and the data collection time are severely penalized. Worse yet, it is difficult to efficiently coordinate between UAVs to improve the performance in terms of WET and WIT. This work proposes to divide UAVs into two teams to behave as data collectors and energy transmitters, respectively. A Multi-Agent Deep Reinforcement Learning (MADRL) method, called TEAM, is leveraged to jointly optimize both teams’ trajectories, minimize the expected Age of Information (AoI), maximize the throughput of IoT devices, minimize the energy utilization of UAVs, and enhance the energy transfer. Simulation results depict that TEAM can effectively synchronize UAV teams and adapt their trajectories while serving a large-scale dynamic IoT environment. [ABSTRACT FROM AUTHOR]

This paper investigates a wireless powered unmanned aerial vehicle (UAV) communication network with backscatter and caching technologies. Specifically, we assume a self-energized UAV with a cache memory is deployed as a flying backscatter device (BD), term the UAV-enabled BD (UB), to relay the source’s signals to the destination. Whereas the source S can act as a wireless charging station or a base station to supply power or transmit information to the UB using the dynamic time splitting (DTS) method. The UAV utilizes its harvested energy for backscattering (i.e., passive communication) and transmit information (i.e., active communication) to the destination. In this context, we aim to maximize the total throughput by jointly optimizing the DTS ratio and the UB’s trajectory with caching capability at the UB. The formulation is troublesome to solve since it is a non-convex problem. To find solutions, we decompose the original problem into two sub-problems, whereas we first optimize the DTS ratio for a given UB’s trajectory and the UB’s trajectory optimization for a given DTS ratio. By using the KKT conditions, a closed-form expression for the optimal value of the DTS ratio is obtained, greatly reducing the computation time. Moreover, the solution of the second sub-problem can be acquired by adopting the successive convex approximation (SCA) technique. Consequently, an efficient alternating algorithm is proposed by leveraging the block coordinate descent (BCD) method. To show the advantages of the proposed BCD-based algorithm, we also provide the solution of the original problem applying the inner approximation (IA) method. Finally, the intensive numerical results demonstrate that our proposed schemes achieve significant throughput gain in comparison to the benchmark schemes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-enabled communication has emerged as an irreplaceable technology in military, disaster relief and emergency scenarios. This correspondence investigates the average throughput in a UAV-enabled cognitive radio network, where the UAV is regarded as a dedicated secondary user to enhance the network coverage and spectral efficiency. Based on the probabilistic line-of-sight channel, we exploit the joint design of UAV trajectory and resource allocation to maximize the average throughput under the constraints of co-channel interference and completion time. The original problem is a mixed integer non-convex problem which is generally NP-hard. We first decompose the primal problem into a bilevel programming problem, and then propose an efficient high-quality algorithm based on the particle swarm optimization approach. The optimized trajectory reveals the trade-off between throughput and co-channel interference. Numerical results verify the superiority of the proposed algorithm as compared to other benchmark schemes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) is a viable solution to remotely charge several energy receivers (ERs) in areas with intermittent access to a source of energy. However, given the limited available energy in the on-board battery of the UAV, which is mainly required to suffice propulsion energy consumption, the UAV struggles to roam seamlessly for long intervals. A key challenge is thus to efficiently use the limited battery energy, and accordingly prolong the UAV’s endurance, via controlling the underlying associated parameters such as cruising velocity. To further extend the battery lifetime, the UAV can also harness other sources of sustainable energy, such as sunlight, via solar energy harvesting. This paper studies a UAV-enabled WPT system, where a rotary-wing UAV transfers radio frequency (RF) wireless energy to several ERs at known locations. To offer uninterrupted seamless service, the UAV also harvests solar energy using solar panels. Considering the propulsion energy consumption of the UAV, we maximize the minimum average power received among all ERs over a finite cycle duration by jointly optimizing the UAV’s time allocation between harvesting and charging, flight trajectory as well as velocity, and transmit power allocation. Due to the non-convexity of the problem, we utilize the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Numerical results show that the proposed solution outperforms benchmark schemes. [ABSTRACT FROM AUTHOR]

Air ground integrated mobile cloud computing (MCC) provides unmanned aerial vehicles (UAVs) the capability to act as an aerial relay with more flexibility and resilience. In the cloud computing architecture, the data generated by ground users (GUs) can be offloaded to the remote server for fast processing. However, the heterogeneity of mobile tasks makes the data size distributed among GUs unbalanced. Besides, the energy efficiency of UAVs movement should be carefully considered for sustainable flight and obstacle avoidance. In general, such a joint trajectory issue can hardly be formulated as a convex optimization in unpredictable and dynamic environments. This paper proposes a potential game combined multi-agent deep deterministic policy gradient (MADDPG) approach to optimize multiple UAVs' trajectory with the consideration of GUs' offloading delay, energy efficiency as well as obstacle avoidance system. In specific, we first model the issue as a mixed integer non-linear problem (MINP), in which the service assignment between multi-user and multi-UAV is solved by potential game. The convergence to a Nash Equilibrium (NE) can be achieved by distributive service assignment update with infinite iteration. Then, we optimize the trajectory with obstacle avoidance at each UAV by MADDPG approach, which has a great advantage of centralized-training and decentralized-execution to reduce the global synchronized communication overhead. UAVs movement can be optimized in continuity rather than other deep reinforcement learning (DRL) approaches generating discrete simple actions. Experiments demonstrate the proposed game-combined learning algorithm can minimize the offloading delay, enhance UAVs’ energy efficiency and avoid the obstacles at the same time. [ABSTRACT FROM AUTHOR]

The explosive computation demands in the Internet of Things (IoT) have triggered the research interests on unmanned aerial vehicle (UAV) assisted mobile-edge computing (MEC) systems even though there are still many challenges, such as computing delay requirement, multi-UAV cooperation, and resource management. This letter focuses on the computing delay issue in MEC systems assisted by multiple UAVs with the goal of task completion time minimization. In particular, both the partial offloading and binary offloading modes are considered by jointly optimizing time slot size, terminal devices scheduling, computation resource allocation, and UAVs’ trajectories. Particularly, an non-LoS channel model is adopted for UAV-ground communication. To handle the formulated problems, we develop alternating optimization algorithms by invoking the successive convex approximation method, Karush-Kuhn-Tucker conditions and penalized method. Numerical results show that the completion time is significantly decreased by the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this letter, we investigate an unmanned aerial vehicle (UAV) enabled wireless powered communication network (WPCN), in which the UAV first employs the radio frequency (RF) signal to power ground users, and then the users communicate with the UAV by using the harvested energy. The sum rate maximization problem in WPCN suffers doubly near-far effect caused by the double signal propagation loss in both the energy harvesting and communication stage. It is generally believed that this effect can be alleviated by integrating UAV into WPCN, thanks to its flexible deployment. However, due to the lack of analytical solution in state-of-art research, it is still unclear how to quantify the advantage that the mobility of UAV brings to migrating the doubly near-far effect. To address this issue, by showing that the optimal UAV trajectory has a multi-location hovering structure, we first derive the closed-form optimal solution of resource allocation, as a function of the UAV hovering location for downlink wireless power transfer (WPT). We then prove that the UAV in our case only needs to hover at a single location for WPT. More importantly, it is explicitly shown that the doubly near far effect can be alleviated into near-far effect. Simulation results validate our analysis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper considers secure communications for an underlay cognitive radio network (CRN) in the presence of an external eavesdropper (Eve). The secrecy performance of CRNs is usually limited by the primary receiver's interference power constraint. To overcome this issue, we propose to use an unmanned aerial vehicle (UAV) as a friendly jammer to interfere Eve in decoding the confidential message from the secondary transmitter. Our goal is to jointly optimize the transmit power and UAV's trajectory in the three-dimensional space to maximize the average achievable secrecy rate of the secondary system. The formulated optimization problem is nonconvex due to the non-convexity of the objective and non-convexity of constraints, which is very challenging to solve. To obtain a suboptimal but efficient solution to the design problem, we first transform it into a more tractable form and then develop an iterative algorithm for its solution by leveraging the inner approximation (IA) framework. Combining tools from IA framework and S-procedure, we further extend the proposed algorithm to a more realistic scenario, where the imperfect location information of ground nodes (including Eve, secondary receiver and primary receiver) is considered, resulting in the average worst-case secrecy rate. Extensive numerical results are provided to demonstrate the merits of the proposed algorithms over existing approaches. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-aided Wireless Rechargeable Sensor Network (WRSN) is a promising application in providing sustainable power supply to the rechargeable sensor nodes (SNs). Constructing a trajectory for the UAV to traverse all SNs with the cheapest cost is an important issue in UAV-aided WRSN. Although some exact algorithms and heuristic methods have been proposed, they cannot achieve a superb result for the large-scale scene within the tolerable time. In this paper, we study the UAV‘s trajectory optimization problem from a novel viewpoint that the designed trajectory should maximize the UAV's energy utilization efficiency. The maximum UAV's energy utilization efficiency problem is decomposed as integer programming and non-convex optimization problems. For the problem that UAV‘s charging position is fixed, we have speeded the algorithm's performance by limiting the search direction, the initial search position, and the search space. In the other case, where the power transfer efficiency is unchangeable within a certain distance, a polynomial-time randomized approximation scheme (PRAS) is presented to find the approximate minimum number of hovering locations. We have presented TPA-FCP and TPA-ERC to solve the above problems, respectively. The numerical results verify that our proposed algorithms effectively reduce the length of optimal trajectory and the time complexity. Besides, the energy carried by the UAV for the specified task is predictable, which provides valuable information for arranging the UAV's flight task. [ABSTRACT FROM AUTHOR]

In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV. [ABSTRACT FROM AUTHOR]

This paper investigates a multiple unmanned aerial vehicles (UAVs) enabled cooperative secure transmission scheme in the presence of multiple potential eavesdroppers. Specifically, multiple source UAVs (SUAVs) send confidential information to multiple legitimate ground users, and in the meantime, multiple jamming UAVs cooperatively transmit interference signals to multiple eavesdroppers in order to improve the legitimate users’ achievable secrecy rate. By taking into account the limited energy budget of UAV, our goal is to maximize the system secrecy energy efficiency (SEE), namely the achievable secrecy rate per energy consumption unit, by jointly optimizing the UAV trajectory, transmit power, and user scheduling under the constraints of UAV mobility as well as the maximum transmit power. The resulting optimization problem is shown to be a non-convex and mixed-integer fractional optimization problem, which is challenging to solve. We decompose the original problem into three sub-problems, and then, an efficient iterative algorithm is proposed by leveraging the block coordinate descent method and Dinkelbach method in combination with successive convex approximation techniques. Simulation results show that the proposed scheme outperforms the other benchmarks significantly in terms of the system SEE. [ABSTRACT FROM AUTHOR]

This paper studies the physical layer security of an unmanned aerial vehicle (UAV) network, where a UAV base station (UAV-B) transmits confidential information to multiple information receivers (IRs) with the aid of a UAV jammer (UAV-J) in the presence of multiple eavesdroppers. We formulate an optimization problem to jointly design the trajectories and transmit power of UAV-B and UAV-J in order to maximize the minimum average secrecy rate over all IRs. The optimization problem is non-convex and the optimization variables are coupled, which leads to the optimization problem being mathematically intractable. As such, we decompose the optimization problem into two subproblems and then solve it by employing an alternating iterative algorithm and the successive convex approximation technique. Our results show that the average secrecy rate performance of the proposed scheme provides about 20% and 150% performance gains over the joint trajectory and transmit power optimization without UAV-J scheme and the transmit power optimization with fixed trajectory scheme at flight period $T=150$  s, respectively. [ABSTRACT FROM AUTHOR]

Motivated by the need to ensure timely delivery of information (e.g., status updates) in the Internet-of-Things (IoT) paradigm, this paper investigates the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak age-of-information for a source–destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. Closed-form solutions for some sub-problems are also provided. One of the sub-problems we solve in this procedure is to jointly optimize the energy and service time allocations for a given trajectory of the UAV. This problem is of interest on its own right because in some cases we may not be able to alter the UAV's trajectory based on the locations of the IoT devices (especially when its primary mission is something else). Our numerical results quantify the gains that can be achieved by additionally optimizing the UAV's trajectory. [ABSTRACT FROM AUTHOR]

This paper investigates unmanned aerial vehicle (UAV) enabled secure communication systems where a mobile UAV wishes to send confidential messages to multiple ground users. To improve the security performance, a cooperative UAV is additionally considered that transmits the jamming signal. In this system, we maximize the minimum secrecy rate among the ground users by jointly optimizing the trajectory and the transmit power of the UAVs as well as the user scheduling. To efficiently solve this nonconvex problem, we adopt block successive upper bound minimization techniques, which address a sequence of approximated convex problems for each block of variables. Numerical results verify that the proposed algorithm outperforms baseline methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are anticipated to be widely deployed in future wireless communications, due to their advantages of high mobility and easy deployment. However, the broadcast nature of air-to-ground line-of-sight wireless channels brings a new challenge to the information security of UAV-ground communication. This paper tackles such a challenge in the physical layer by exploiting the mobility of UAV via its trajectory design. We consider a UAV-ground communication system with multiple potential eavesdroppers on the ground, where the information on the locations of the eavesdroppers is imperfect. We formulate an optimization problem, which maximizes the average worst case secrecy rate of the system by jointly designing the robust trajectory and transmit power of the UAV over a given flight duration. The nonconvexity of the optimization problem and the imperfect location information of the eavesdroppers make the problem difficult to be solved optimally. We propose an iterative suboptimal algorithm to solve this problem efficiently by applying the block coordinate descent method, $\mathcal {S}$ -procedure, and successive convex optimization method. Simulation results show that the proposed algorithm can improve the average worst case secrecy rate significantly, as compared to two other benchmark algorithms without robust design. [ABSTRACT FROM AUTHOR]

In future mobile networks, it is difficult for static base stations (BSs) to support the rapidly increasing data services, especially for cell-edge users. Unmanned aerial vehicle (UAV) is a promising method that can assist BSs to offload the data traffic, due to its high mobility and flexibility. In this correspondence, we focus on the UAV trajectory at the edges of three adjacent cells to offload traffic for BSs. In the proposed scheme, the sum rate of UAV-served edge users is maximized subject to the rate requirements for all the users, by optimizing the UAV trajectory in each flying cycle. The optimization is a mixed-integer nonconvex problem, which is difficult to solve. Thus, it is transformed into two convex problems, and an iterative algorithm is proposed to solve it by optimizing the UAV trajectory and edge user scheduling alternately. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]