To address the waste of spectrum resources caused by the separate operation of integrated sensing and communication (ISAC) and mobile edge computing (MEC), the integrated sensing, communication, and computing (ISCC) paradigm has been proposed. However ISCC systems are still facing serious channel fading and obstacle occlusion problems, which are expected to be solved by the emerging simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In this paper, we propose a STAR-RIS-assisted ISCC framework, where the computational tasks from users are offloaded to a base station (BS) for processing with the assistance of a STAR-RIS, and the results are subsequently downloaded back to the corresponding users. Notably, target sensing operation is executed concurrently throughout the process of result downloading. To enhance communication efficiency, the non-orthogonal multiple access (NOMA) scheme is incorporated. The number of offloading bits of users is maximized by optimizing the uplink-downlink resource of the network, including the received beamforming vector, active beamforming vector, time slot, phase shift of STAR-RIS, and computational resource. In order to solve the highly complex non-convex problem, we employ the block coordinate descent (BCD) framework to decompose the proposed problem into three subproblems. The convex-concave procedure (CCCP) method is used to deal with the nonconvex constraints. For the rank-1 constraints, the semidefinite relaxation (SDR) approach and the penalty method are invoked. The numerical results show that: 1) the application of STAR-RIS significantly improves the offloading capability of the network, 2) the NOMA scheme significantly outperforms the orthogonal multiple access (OMA) scheme, and 3) comparing with other benchmark schemes, the proposed algorithm significantly improves the overall network performance.

This paper explores cargo delivery strategies and path planning for unmanned aerial vehicle (UAV) assigned with kinematically-constrained multi-user pickup and delivery operations. Specifically, the UAV is programmed to depart from a warehouse to complete user-generated orders and then delivery packages to designated user locations. To ensure the safety and efficiency of UAV operations, it is essential that the cargo UAV maintain a reliable connection with ground base stations (GBSs) throughout the entire flight missions. Our aim is to simultaneously enhance both the cargo delivery efficiency and energy efficiency of the system by jointly optimizing the delivery sequence and flight trajectories of the UAV. Initially, we create an urban radio map, which serves as the foundation for implementing a simulated particle swarm optimization (SPSO) algorithm. This approach efficiently determines the optimal sequence of UAV visits to the warehouse and user locations. Subsequently, using these sequences, we apply a hybrid grid search algorithm (HGSA) to meticulously plan the trajectory between pickup and delivery locations according to our objective function. Simulations comparing traditional traveling salesman problem (TSP) models and our proposed time and energy-constrained TSP with kinematic constraints (TETSPKC) show that our approach offers significant optimization advantages.

In this paper, we propose an unmanned aerial vehicle (UAV) assisted integrated sensing and communication (ISAC) system for the Internet of Things (IoT), where the UAV beams simultaneously sense the status information of multiple IoT sensing nodes and transmit the sensing data to a data center. The main objective is to enable the UAV to perform sensing using radar beams and then transmit the collected information to the data center via communication beams. To evaluate the sensing performance of the ISAC system, we introduce radar mutual information as a key metric from an information-theoretic perspective. Considering the mutual interference between sensing and communication as well as the communication rate limitations, we aim to maximize radar mutual information by optimizing the UAV flight trajectory and transmit beamforming. To address this problem, we propose a joint optimization algorithm that employs semi-finite and concave programming techniques to maintain rank-1 constraints with low complexity. Numerical results demonstrate the effectiveness of the proposed algorithm in achieving the maximum radar estimation rate, thereby validating the rationality and feasibility of the proposed design approach.

In this paper, a cellular-connected uncrewed aerial vehicles (UAVs) pickup and delivery system is studied in which the multiple UAVs are served by ground base station (GBS). These UAVs commence operations from the hangar, systematically execute a sequence of pickups and deliveries before returning, all while maintaining a continuous and reliable communication link with the GBS throughout their missions. Owing to the limited onboard energy resources of UAVs and the influence of task sequence and payload characteristics on UAV’s energy consumption, this study focuses on minimizing energy usage through the optimization of task sequences and flight trajectories. Firstly, a radio map of the operational area is constructed to identify flight zones that ensure reliable communication, an improved Dijkstra algorithm is then proposed to compute the shortest viable path between any two access points that satisfy reliable communication criteria. Furthermore, a chromosome structure tailored for a hybrid genetic algorithm (HGA) is devised to address the complexities of multi-UAV task allocation. With the aid of a developed distance matrix, the HGA is utilized to determine the optimal delivery sequence, thereby achieving the objectives of minimizing the maximum energy consumption (MME) and minimizing the sum energy consumption (MSE) respectively. Finally, simulation analyses demonstrate that the proposed MME and MSE optimization strategies enable approximately a 50% reduction in peak flight energy consumption and around a 40% decrease in total energy consumption, compared to multi-UAV pickup and delivery systems without energy optimization.

The evolution towards Internet of Things (IoT) in the forthcoming sixth generation (6 G) is facing massive amounts of transmitted data and harsh wireless transmission environment, which severely degrade the quality of communication. To overcome these difficulties, a novel multiple intelligent reflecting surfaces (IRSs) assisted unmanned aerial vehicle (UAV) network framework with non-orthogonal multiple access (NOMA) is proposed in this article, where the UAV applies the NOMA scheme to deliver the information to the ground users assisted by multiple IRSs. We aim to maximize the achievable rate of the considered network while guaranteeing the minimum communication rate of each user, by jointly optimizing the multi-IRS phase shifts, UAV transmit power, UAV trajectory, and NOMA decoding order. To handle the coupled variables and integer constraints, we decompose the original problem into three subproblems based on the block coordinate descent (BCD) framework. Specifically, we first obtain the multi-IRS phase shifts by applying the semidefinite relaxation (SDR) technique. Next, the UAV transmit power allocation is derived by exploiting the concave convex procedure (CCCP) method. The UAV trajectory and NOMA decoding order are finally obtained by invoking the penalty-based method and the successive convex approximation (SCA) technique. Based on these, an alternating optimization algorithm is proposed. The numerical results show that: 1) the NOMA scheme enhances the utilization of the spectrum and enhances the access capacity of the communication system; 2) the multi-IRS cooperative structure increases the reflective channels and effectively improves the air-ground transmission environment, thus enhancing the system achievable rate; 3) the proposed multi-IRS assisted UAV NOMA algorithm achieves a significant network rate improvement compared to other benchmark schemes.

Cargo Unmanned Aerial Vehicles (UAVs) have been increasingly utilized in short-distance logistics scenarios within urban environments. This paper delves into the path planning problem for multi-user pickup and delivery operations of UAVs. The UAVs embark from a depot, execute the pickup and delivery tasks, and subsequently return to the depot, all while maintaining a reliable connection with the ground base station (GBS) throughout their flight. The energy consumption of the UAVs is intricately tied to the sequence of pickup and delivery, as well as the weight of the transported goods. To minimize the maximum energy consumption of the UAVs and optimize their flight trajectories, this paper focuses on optimizing the access sequence. Firstly, we construct a radio map of the target area, enabling us to assess communication reliability. Subsequently, we introduce an enhanced Dijkstra’s algorithm to compute the shortest paths between any two access points that ensure reliable communication. Then, we devise a chromosome structure tailored for a hybrid genetic algorithm (HGA), specifically addressing the multi-UAV pickup and delivery problem. Leveraging the constructed distance matrix and the designed chromosome structure, we apply the HGA to solve the problem of minimizing the maximum energy consumption during multi-UAV pickup and delivery operations. Finally, we validate the effectiveness of our proposed method through simulation results.

In this paper, we propose an inspection system based on a cellular-connected unmanned aerial vehicle (UAV), where UAV departs from the starting point and flies to the inspection points for patrol inspection. The purpose of our work is to minimize the total inspection service time of the UAV, while ensuring that the total communication outage time throughout the flight is less than a certain threshold. The total inspection service time encompasses the cumulative time spent by the UAV traveling to each inspection point. To address the non-convex problem, we propose a hybrid algorithm that combines deep reinforcement learning (DRL) with simulated annealing (SA). Initially, we employ DRL to determine the trajectory between any two points within the defined scenario, followed by the utilization of SA to derive the optimal inspection sequence. The numerical results show that the total inspection service time obtained by our proposed algorithm is always lower than the benchmark algorithm, and the effect is better when the threshold is smaller, about 10% lower than the benchmark algorithm.

In this paper, we consider an unmanned aerial vehicle (UAV)-based patrol inspection scenario, where the cellular-connected UAV traverses multiple pre-determined waypoints for data collection, and then offloads computation task to the ground base stations (GBSs). This paper aims to minimize the sum of the total energy consumption by jointly optimizing the task completion time, communication scheduling, computation resource allocation, and UAV's trajectory. First, we decompose the original problem into two trackable subproblems: 1) design the optimal traverse order among cruise points; and 2) determine the optimal transmission strategy between two consecutive cruise points. Then, by involving the communication rate performance and the topology construct among the GBSs and the cruise points, a novel weighted factor of the edge is proposed to design the traverse order, which can be compatible with the light and heavy task offloading scenarios. The successive convex approximation (SCA) technique and block coordinate descent (BCD) framework are adopted to optimize the UAV's trajectory and the wireless resource allocation. The numerical results finally indicate that our proposed transmission strategy solution decreases the total energy consumption in various scenarios and outperforms other benchmark schemes.

In this paper, we consider a multi-user oriented UAV cargo delivery system, cellular-connected UAV fly to all users within the distribution range successively from the starting point to deliver goods. The UAV needs to complete its mission quickly and maintain good communication with ground base stations (GBSs). To satisfied the above requirements, we propose a three-step approach. Firstly, the influence of cargo weight on UAV energy consumption is considered, we propose a weight change travel salesman problem (WCTSP) to desgin initial trajectory. Secondly, the entire flight trajectory is divided into a series of sub-trajectories base on the obtained initial trajectory. Finally, deep reinforcement learning (DRL) is adopted to optimize all the subtrajectives. By setting reasonable neural network parameters and reward function, the optimal trajectory under the current standard can be obtained after the neural network is trained continuously until it converges. This paper aims to minimize the weighted sum of total energy consumption and total outage time by jointly optimizing cargo distribution scheduling, communication scheduling and UAV flight strategy. The simulation results demonstrate the effectiveness of our proposed trajectory optimization scheme.

In this study, energy cooperation schemes are considered in wireless cooperative networks; in these networks multiple pairs of users communicate with each other, assisted by an energy harvesting relay that gathers energy from the received signal by applying a power splitting scheme and forwards the received signal by using the harvested energy. This study is focused on the energy cooperation strategies for the relay to distribute the harvested energy between the multiple user pairs in amplify‐and‐forward and in decode‐and‐forward modes. Specifically, optimal solutions without energy cooperation at the relay node are first proposed and then the authors formulate the energy cooperation optimisation problem. This optimisation problem is non‐convex. They propose an iterative energy cooperation solution to maximise the system throughput by assigning the proper harvested energy to each user pair for both types of forwarding models. They show that with the proposed method, the update in each iteration consists of a group of convex problems with a continuous parameter. Moreover, they derive the optimal solution to these convex problems in closed‐form and it is shown that the solution can converge to a local optimum. Simulation results demonstrate that the proposed algorithm outperforms the traditional non‐cooperation method.