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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. [ABSTRACT FROM AUTHOR]

In this paper, we study a joint placement, resource allocation, and user association problem for UAV-assisted wireless networks with constrained backhaul links, where multiple UAV-mounted base stations (UBSs) are deployed to provide wireless services for ground users. We propose a novel framework to maximize the user throughput within the flight-time of UBSs and provides fairness among the users. We first obtain the optimal resource allocation schemes based on different fronthaul and backhaul conditions, and an efficient iterative algorithm is then developed to jointly optimize user association and UBS placement. The optimal UBS placement can be achieved by solving an unconstrained optimization problem which is a simplification of the initial constrained optimization problem based on the optimal resource allocation. We develop a dual-domain coordinated descent and bipartite graph matching based sub-process to identify an optimal user association that prefers the nearby UBSs, as the user association under constrained backhaul links have non-unique optimal solutions. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm, and results show that our proposed method under constrained backhaul can improve both the average throughput by 49% and the fairness among the users by 47% in comparison with the method under ideal backhaul. [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]

This paper proposes a secure downlink multi-user transmission scheme enabled by a flexible unmanned aerial vehicle base station (UAV-BS) and non-orthogonal multiple access (NOMA). According to their heterogeneous service requirements, multiple legitimate users are categorized as security-required users (SUs) and quality of service (QoS)-required users (QUs), while these QUs can potentially act as internal eavesdroppers which are curious about the secrecy transmissions of SUs. In such a context, our goal is to maximize the achievable minimum secrecy rate among SUs through the joint optimization of user scheduling, power allocation, and trajectory design, subject to the QoS requirements of QUs and the mobility constraint of UAV-BS. Due to the non-convexity of the problem, an efficient iterative algorithm is firstly proposed, based on the alternative optimization (AO) and successive convex approximation (SCA) methods and along with a penalty-based algorithm to deal with the introduced binary integer variables, to obtain a sub-optimal solution. Then, we propose an SUs-oriented low-complexity algorithm by taking advantage of the inherent characteristics of the optimization problem, which can efficiently reduce the computational complexity and can act as a reasonable initial solution for the previous iterative algorithm to achieve better performance. Finally, the superiority of our proposed scheme compared with the conventional orthogonal multiple access (OMA) one is validated by numerical simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we study an UAV base station (UBS) assisted cellular network that consists of a single ground macro base station (MBS), multiple UBSs, and multiple ground terminals. We assume that the MBS transmits to the UBSs and the terminals in the licensed band while the UBSs use a separate unlicensed band (e.g., Wi-Fi) to transmit to the terminals. For the utilization of the UBSs, we propose a cooperative decode–forward (DF) protocol in which multiple UBSs assist the terminals simultaneously while maintaining a predetermined interference level on the coexisting unlicensed band users. For our network setup, we formulate a joint optimization problem for minimizing the aggregate gap between the target rates and the throughputs of terminals by optimizing over the 3D positions of the UBSs and the resources (power, time, bandwidth) of the network. To solve the optimization problem, we propose an efficient nested structured algorithm based on particle swarm optimization and convex optimization methods. Extensive numerical evaluations of the proposed algorithm is performed considering various aspects to demonstrate the performance of our algorithm and the gain for utilizing UBSs. [ABSTRACT FROM AUTHOR]