The sixth-generation (6G) communication requires supporting massive Internet of Things (IoT) devices and extremely differentiated IoT applications for the air–space–ground integrated network. Relying on the aerial superiority, unmanned aerial vehicle (UAV) is capable of acting as an aerial base station (BS) and supporting IoT deployment in remote and disaster areas. A UAV-supported clustered nonorthogonal multiple access (C-NOMA) system is put forward in this article. Specifically, the UAV provides services to IoT terminals as an aerial BS based on the wireless-powered communication (WPC) technique. According to this system, we propose a synergetic scheme for UAV trajectory planning and subslot allocation. Our goal is to maximize the uplink average achievable sum rate of IoT terminals by synergistically planning UAV trajectory and subslot duration, while guaranteeing the uplink achievable sum rate and the UAV mobility constraints. As the formulated problem suffers nonconvexity and complication, an efficient iterative algorithm is proposed to address it. First, for fixed UAV trajectory, all the terminals are clustered and a subslot allocation algorithm based on the Lagrange multiplier and bisection method is proposed. Then, for a fixed clustering state and subslot duration, we optimize the UAV trajectory. Finally, we solve these two subproblems alternatively until the objective function converges. The effectiveness of the proposed scheme in the UAV-supported C-NOMA system is verified by the numerical results.

Unmanned aerial vehicles (UAVs) equipped with integrated sensing and communication (ISAC) technology have been employed in the field of communication. In this paper, we propose an ISAC-UAV assisted secure communication system amidst multiple eavesdroppers. In each time slot, we implement ISAC and communication functions. Since there are multiple eavesdroppers in the system, jammer UAV is required to send jamming signals to the eavesdroppers. During the ISAC process, the source UAV identifies multiple eavesdroppers, relaying their position information to a jammer UAV. During the communication process, multiple eavesdroppers attempt to intercept the communication link between source UAV and users. We use the center of all users as the horizontal location of the jammer UAV. To maximize the secure data transmit rate, we propose a joint optimization problem involving user scheduling, transmit power, and source UAV trajectory. We break down the non-convex optimization problem into three subproblems, and then achieve the suboptimal solution to the original problem through iteratively optimizing these subproblems. Simulation results indicate that the proposed solution can achieve significant secure transmit rate even in the presence of multiple eavesdroppers.

Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle filter. However, the particle filter is unsuitable for real time localization on resource-limited smartphones, since it is rather time-consuming and computationally expensive. On the other hand, the light computation fusion approaches including Kalman filter and its variants are inapplicable, since an explicit RSS-location measurement equation and the related noise statistics are unavailable. This paper proposes a novel data fusion framework by using an extended Kalman filter (EKF) to integrate WiFi localization with PDR. To make EKF applicable, we develop a measurement model based on kernel density estimation, which enables accurate WiFi localization and adaptive measurement noise statistics estimation. For the PDR system, we design another EKF based on quaternions for heading estimation by fusing gyroscopes and accelerometers. Experimental results show that the proposed EKF based data fusion approach achieves significant localization accuracy improvement over using WiFi localization or PDR systems alone. Compared with a particle filter, the proposed approach achieves comparable localization accuracy, while it incurs much less computational complexity.

As unmanned aerial vehicle (UAV) communication has been widely used in all walks of life, its secrecy issue has also received more and more attention. This article studies the physical-layer security of UAV relaying communication system in multiterminal Internet of Things (IoT) scenarios. Specifically, while receiving the information from the ground base station, the UAV safely forwards the information to one of a group of IoT terminals in the presence of an eavesdropper. Under the constraints of information causality and UAV mobility, our goal is to maximize the minimum average secrecy rate among all IoT terminals. Based on the nonconvex problem, this article proposes a high-efficiency algorithm for joint optimization of UAV trajectory and resource allocation. The simulation results show that the proposed algorithm not only effectively improves information secrecy of IoT terminals, but also enhances the fairness of communication between the IoT terminals.

The 6th generation (6G) communication envisions a highly integrated network where aerial vehicles connect satellites and terrestrial systems. As low altitude vehicle, unmanned aerial vehicle (UAV) is able to quickly establish wireless networks without resorting to terrestrial infrastructure. Due to strong invulnerability, synergistic cooperation and flexible scheduling, the multi-UAV communication system can significantly improve system performance through collaboratively designing multi-UAV trajectory and radio resource scheduling. This article proposes a multi-UAV wireless powered communication (WPC) system for 6G-enabled Internet of Things (IoT). Specifically, each time slot is split into uplink and downlink subslot. In the downlink subslot, multiple UAVs dispatched as aerial communication platforms transfer energy to multiple IoT users. In the uplink subslot, the association between UAVs and users is designed, and then the scheduled user uploads data to the specific UAV by using the harvested energy. According to the proposed system, we propose a collaborative scheme of multi-UAV trajectory optimization and resource scheduling. By synergistically optimizing UAV-user association, subslot duration, user transmit power, and multi-UAV trajectory, we maximize the minimum average achievable rate among all users. Particularly, the nonconvex optimization problem can be efficiently figured out by an alternative iteration algorithm proposed in this article. Finally, numerical results show that our design can not only optimize multi-UAV flight path, but also achieve higher objective value than benchmark schemes.

Cooperative coordination at unsignalized road intersections, which aims to improve the driving safety and traffic throughput for connected and automated vehicles (CAVs), has attracted increasing interests in recent years. However, most existing investigations either suffer from computational complexity or cannot harness the full potential of the road infrastructure. To this end, we first present a dedicated intersection coordination framework, where the involved vehicles hand over their control authorities and follow instructions from a centralized coordinator. Then a unified cooperative trajectory planning problem will be formulated to maximize the traffic throughput while ensuring driving safety. To address the key computational challenges in the real-world deployment, we reformulate this non-convex sequential decision-making problem into a model-free Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement learning (DRL) framework. Simulation and practical experiments show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve the traffic throughput in the realistic continuous traffic flow. The most remarkable advantage is that our strategy could reduce the time complexity of computation to milliseconds, and is shown scalable when the road lanes increase.