In this paper, we consider a reconfigurable intelligent surface (RIS) assisted unmanned aerial vehicle (UAV) relaying communication system, where the RIS is mounted on the UAV and can move at a high speed. Compared with the conventional static RIS, better performance and more flexibility can be achieved with the assistance of the mobile UAV. We maximize the average downlink throughput by jointly optimizing the UAV trajectory, RIS passive beamforming and source power allocation for each time slot. The formulated non-convex optimization problem is decomposed into three subproblems: passive beamforming optimization, trajectory optimization and power allocation optimization. An alternating iterative optimization algorithm of the three subproblems is proposed to achieve the suboptimal solutions. The numerical results indicate that the RIS-UAV relaying communication system with trajectory optimization can get higher throughput.

High-mobility unmanned aerial vehicles (UAVs) can serve as dual-function aerial service platforms for the Internet of Things (IoT), providing both sensing and communication services for IoT nodes without a base station (BS), particularly in emergency situations. In this paper, a UAV-assisted integrated sensing and communications (ISAC) system is proposed for IoT, which simultaneously senses the status information around the IoT and sends the sensing information to both the IoT nodes and a data collection center. In order to assess the sensing performance of ISAC, the radar estimation rate is introduced as a significant metric from the perspective of information theory. Considering the mutual interference between sensing and communications, the radar estimation rate is maximized through the coordinated optimization of UAV task scheduling, transmit power allocation, and 3D flight parameters under the constraint of communication rate. The formulated non-convex mixed-integer programming problem is divided into three subproblems, including UAV task scheduling optimization, UAV sensing and communication power optimization, and UAV 3D flight parameters optimization. The optimal solutions can be achieved by proposing a three-layer iterative optimization algorithm to optimize the three subproblems iteratively. The simulation results show that the radar estimation rate can well measure the sensing performance of the ISAC, which can be effectively improved by optimizing the 3D UAV flight parameters.

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.

Integrated sensing and communication (ISAC) is developing rapidly due to the advantages of bandwidth saving, hardware cost reduction and energy saving. This paper proposes a unmanned aerial vehicle (UAV) enabled ISAC (UAV-ISAC) system, where the UAV will sense ground users and forward sensed information to base station (BS). Radar mutual information (MI) is introduced to measure the UAV sensing performance. On the basis of sensing fairness for each user, a multi-objective resource optimization problem of maximizing both energy efficiency (EE) and minimum radar MI is studied by jointly optimizing user scheduling, transmit power and UAV trajectory. To solve the non-convex optimization problem, we decompose it into three sub-problems: user scheduling optimization, transmit power optimization, and UAV trajectory optimization. Each subproblem can be solved by successive convex approximation (SCA), fractional programming and relaxation technique. By iteratively optimizing the three sub-problems, we can obtain the suboptimal solution to original optimization problem. Simulation results show that the proposed optimization scheme can maximize the EE of UAV while ensuring sensing fairness for all users.

Unmanned aerial vehicle (UAV) has been widely used as an aerial base station (BS) to assist the ground communications for Internet of Things (IoT) due to its wide-area coverage, high-mobility, and Line-of-Sight (LoS) communication link. In this article, a multi-UAV enabled IoT is considered, where the UAVs provide integrated sensing and communication (ISAC) services to the IoT nodes. The radar mutual information (MI) is introduced to measure the sensing performance of ISAC from the information theory perspective. To achieve fair communications, we seek to maximize the minimum communication rate per IoT node by jointly optimizing node scheduling, transmit power and 3-D trajectory of the UAV under the constraint of radar MI for each IoT node. The formulated nonconvex multivariable optimization problem is divided into three subproblems, including UAV scheduling optimization, UAV transmit power optimization, and UAV 3-D trajectory optimization. The near-optimal solutions of the original optimization problem can be achieved by proposing a three-layer iterative optimization algorithm to optimize the three subproblems iteratively. The simulation results demonstrate that the proposed optimization scheme can obviously improve communication rate under the constraint of radar MI as well as achieve fair communication for each node.