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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)-aided two-way relay networks with multiple terrestrial user pairs is investigated, where a fix-wing UAV is served as a two-way relay to assist information transmission. We aim to maximize the total rate of the two-way relay networks while the quality of service (QoS) being guaranteed. A novel relay strategy, namely time-slots pairing, is proposed by exploiting physical-layer network coding (PNC) protocol. Specifically, the UAV receives and buffers the signals from the scheduled user pair when the UAV approaches one of them, then forwards the modulated signal when the UAV is close to the other. As a result, a non-convex total rate maximization problem is formulated by joint trajectory design and resource optimization. To solve this problem, we first decompose it into three sub-problems, and then a three-step iterative algorithm that can effectively handle the non-convex problem with no worse than a polynomial complexity is developed leveraging the block coordinate descent (BCD) technique and successive convex approximation (SCA) method. Simulation results finally illustrate that: 1) Compared with amplify-and-forward (AF) or decode-and-forward (DF) protocols, the proposed design based on PNC protocol can significantly improve the total rate of the two-way relay networks; 2) the time-slots pairing relay strategy always achieves a significant enhancement on performance than the traditional non-time-slots pairing relay strategy; 3) the time-slots pairing relay strategy is more favorable for the mobile PNC relay networks since it can well resist the degradation of the entire networks caused by users’ increased service quality requirements. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have attacted much attention in the field of wireless communication due to its agility and altitude. UAVs can be used as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground devices (GDs) in various scenarios, such as emergency communication and traffic offloading in hotspots. However, due to the limited communication ranges and high prices of commercial UAV-BSs, covering a target area all the time with sufficient UAVs is quite challenging, especially under dynamic environment. We need to design the trajectory of the UAV-BSs to optimize system performance. Most existing works focus on the energy-efficient coverage and throughput maximization but ignore the fairness of communication service, especially the fairness at user-level. Besides, reinforcement learning is suitable for solving decision problems in dynamic environments. However, most existing works use centralized deep reinforcement learning (DRL) approaches. Due to the scalability and low time complexity, a distributed DRL approach is more suitable for multiple UAV-BSs communication system in dynamic environment. Unlike previous works, we characterize the fairness at user-level based on proportional fairness scheduling and formulate a weighted-throughput maximization problem via designing UAV-BSs’ trajectory. Then we model the dynamic deploymentproblem of UAV-BSs as a Markov game and propose a multi-agent deep reinforcement learning-based distributed UAV-BSs control approach named MAUC. MAUC approach adopts the framework of centralized training with distributed execution. Simulation results show that the MAUC can improve fairness of communication service by sacrificing a small amount of throughput. [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]

In the finite blocklength scenario, which is suitable for practical applications, a method of maximizing the average effective secrecy rate (AESR) is proposed for a UAV-enabled secure communication by optimizing the UAV's trajectory and transmit power subject to the UAV's mobility constraints and transmit power constraints. To address the formulated non-convex optimization problem, it is first decomposed into two non-convex subproblems. Then the two subproblems are converted respectively into two convex subproblems via the first-order approximation. Finally, an alternating iteration algorithm is developed by solving the two subproblems iteratively based on successive convex approximation (SCA) technique. Numerical results show that our proposed scheme can achieve a 6% to 13% improvement on the AESR performance compared with the benchmark schemes. [ABSTRACT FROM AUTHOR]

Due to the small size, low cost, and autonomous nature, unmanned aerial vehicles (UAVs) have attracted growing attention in improving traditional networks’ performance. This paper considers a multi-UAV enabled large-scale wireless recharge networks, where a group of UAVs is dispatched as mobile wireless power transfer, and information collection systems to serve a set of ground low-power sensor nodes. Specifically, the UAVs employ radio frequency (RF) wireless power transfer (WPT) to supply energy towards the sensor nodes, and the communication services are available via wireless information transfer (WIT) systems. To achieve balanced energy consumption among UAVs, we maximize the energy utilization efficiency of UAVs, and minimize the communication delay by optimizing the trajectory jointly with constraints of the energy capacity, and the area of the target region. The formulated problem is a mixed-integer programming problem that is a variation of multiple traveling salesman problem. Thus we present a heuristic algorithm that combines the evolutionary algorithm, and variable neighborhood search to achieve the optimal visited sequence of the sensor nodes. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm. It draws new insights on the estimation of the feasibility of the given UAVs whose energy capacity is limited. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the past several years, unmanned aerial vehicle (UAV) have been employed to provide enhanced coverage or relay service to mobile users in a scenario with limited or even no infrastructure since they can be deployed to almost everywhere and can be manipulated at anytime. This paper studies UAV as aerial base station (BS) enabled wireless communication system, where a UAV is dispatched to provide wireless communication service to a set of ground users with difference quality-of-experience (QoE) requirements. In real world, user requirements are randomly and unevenly distributed. In addition, UAV communication coverage and on-board energy are limited and system resources are also limited (e.g., transmission power, spectrum). In order to meet the QoE of all users with limited system resources and limited UAV energy, we jointly optimize user communication scheduling, UAV trajectory, transmit power and bandwidth allocation to maximize energy-efficiency and satisfy user QoE requirement. The formulated problem is mixes integer non-convex and non-concave so it is difficult to solve. In this paper, we solvevv the problem with two steps as follows. Firstly, we transform the objective function into a tractable form. Secondly, we divide the optimal problem into four sub-optimal problems, and then use a powerful iterative algorithm with the Dinkelbach and block coordinate descent to solve the optimal problem. That is to say, the user scheduling, UAV trajectory, transmission power and bandwidth allocation are alternately optimized in each iteration. Extensive simulation results present that our proposed method can obtain higher energy efficiency than that of baselines. Specifically, the energy efficiency obtained by our proposed method is 12.5% higher than the approach that only maximizes throughput. [ABSTRACT FROM AUTHOR]

This paper considers the wireless powered communication networks (WPCNs) where one hybrid sink (H-sink) coordinates the wireless energy/information transmissions to/from a set of one-hop nodes powered by the harvested radio-frequency energy only, and aims to maximize the common throughput (CT) of nodes. Specifically, we investigate the WPCN using non-orthogonal multiple access, where all nodes transmit information simultaneously to H-sink and H-sink performs successive interference cancellation to alleviate the noise from other nodes. Energy transfer duration, data transmit powers of nodes and the decoding order of nodes’ signals are jointly optimized to maximize the CT. We first formulate the CT maximization (CTM) as a non-convex optimization problem, then separate the CTM problem into a number of subproblems to solve, and finally recover the optimal solution of the original problem from the optimal solutions of subproblems. Our CTM scheme significantly outperforms the available scheme using up all harvested energy. Furthermore, we propose an efficient algorithm to maximize the sum throughput while guaranteeing that the maximum CT is achieved. This scheme further greatly increases the sum throughput. [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]