Recently, fixed-wing unmanned aerial vehicles (UAVs) are able to extend the communications mission time and ease of deployment due to their powerful onboard capabilities and flexibility, and reflective intelligent surfaces (RISs) are capable of reflecting links to avoid obstacles and thus improve channel gain. Therefore, RIS-assisted fixed-wing UAVs are widely used in wireless communications. Nevertheless, fixed-wing UAVs have limited energy, so improving energy efficiency is critical. This article focuses on energy efficiency optimization problems under RIS-assisted fixed-wing UAV communications. In communications coverage systems, the flight trajectory of fixed-wing UAVs and service scheduling to ground nodes (GNs) significantly impact energy efficiency. Existing work often adopts circular trajectory and traditional deep reinforcement learning (DRL) algorithms for optimizing trajectory and service scheduling. However, circular trajectory can not adapted to the GN distribution well. In addition, the traditional DRL algorithm has two drawbacks: 1) the efficiency of exploring the empirical process is low and 2) the accuracy of handling the hybrid action space needs to be higher. Thus, we propose the midpoint iteration convex hull (MICH) algorithm based on the Graham scan to design trajectories that can be adapted to the distribution of the GNs. In addition, we propose the action screening virtual and real experience (AS-VRE) mechanism and the N-steps hybrid deep Q and policy network (NsHQPN) algorithm to address the low-exploration efficiency and the low-fetch accuracy in handling the hybrid action space. Experiments show that our proposed MICH algorithm, AS-VRE mechanism, and NsHQPN algorithm can effectively improve the system energy efficiency and outperform other baseline schemes.

Reconfigurable-intelligent-surface (RIS)-assisted autonomous aerial vehicles (AAVs) communications technology improves energy efficiency by reflecting signals. This article utilizes RIS and deep reinforcement learning (DRL) to optimize the scheduling of ground terminals (GTs), AAV trajectories, resource allocation, and time slot lengths to maximize system energy efficiency. Three flaws of the existing DRL algorithm are also addressed to seek higher energy efficiency further. First, DRL faces exploration challenges due to the complexity of the solution space, resulting in low rewards. We propose the ant colony DRL (ACDRL) algorithm, which optimizes the scheduling order of the GTs using the ant colony optimization (ACO) algorithm and feeds the results back to the DRL to optimize the subsequent decision making, thus reducing the exploration overhead. Second, to reduce the degree of local optimization when dealing with hybrid action space planning, we propose a hybrid discrete-continuous DRL (HDCDRL) algorithm to improve action accuracy. Finally, to better generalize the model to similar tasks, we propose the transfer-DRL (T-DRL) model to reduce the training time when the task changes. Experimental results show that our proposed solution outperforms the benchmark solution.

Satellite-terrestrial integrated networks have recently gained substantial interest due to their exceptional coverage, lower transmission delay, robust storage, and computing power. However, existing task offloading schemes often fail to effectively manage task dependencies, resulting in incorrect execution sequences, increased end-to-end delay, and excessive energy consumption. To address these challenges, we propose a dependency-aware task offloading framework for jointly optimizing delay and energy consumption in satellite-terrestrial collaborative networks with mobile-edge computing (MEC). First, we construct a directed acyclic graph (DAG) based dependency-aware task offloading framework aimed at reducing delay and energy consumption. Second, to reduce the frequency of low Earth orbit (LEO) satellite access, we design a cluster head selection strategy (CHSS), which leverages DAG-based task dependencies to optimize the association between Internet of Things (IoT) devices and LEO satellites. Finally, we formulate system delay and energy consumption as a cost-minimization problem, modeling it as a Markov decision process (MDP). We also propose a novel hybrid deep reinforcement learning (DRL) algorithm to effectively handle DAG structures and optimize task offloading decisions, thereby minimizing the total cost. Extensive simulation results confirm the effectiveness of the proposed method, demonstrating that the proposed algorithm significantly outperforms others by reducing system delay by 18.07% and decreasing energy consumption by 21.15% on average, respectively.

With the development of artificial intelligence technology, Autonomous aerial vehicles (AAV) have the ability to sense the environment. multiple object tracking (MOT) in AAV video is a very important vision task with a wide variety of applications. However, there are still many challenges in MOT in AAV video. First, the movement of the onboard camera in the three-dimensional (3-D) direction during the tracking process, as well as the unpredictable measurement noise characteristics of AAVs flying at high speeds, can lead to significant deviations in the prediction of the object’s position. Second, the applicability of the traditional detection algorithm decreases when the object is small and dense in the AAV viewpoint during detection. Finally, the traditional intersection over union (IoU) matching approach does not take into account the effects of the height and width of the box, and the matching results are inaccurate for the prediction and detection box. In order to address these challenges, we recommend the adaptive DeepSort (ADT) algorithm to reduce the prediction bias due to camera movement and difficulty in predetermining measurement noise characteristics, the hybrid attention transformer-YOLOv8 (HAT-YOLOv8) algorithm to enhance the detection capability of tiny objects, and the IoU of height and width (HWIoU) matching algorithm, which improves the matching accuracy and thus the tracking accuracy. Experimental results show that our proposed solution outperforms the baseline solution. It outperforms the current mainstream StrongSort in MOTA, HOTA and IDF1 by 2.86%, 0.9%, and 9.36%. Code repository link:https://github.com/networkcommunication/.

Radiation therapy is regarded as the mainstay treatment for cancer in clinic. Kilovoltage cone-beam CT (CBCT) images have been acquired for most treatment sites as the clinical routine for image-guided radiation therapy (IGRT). However, repeated CBCT scanning brings extra irradiation dose to the patients and decreases clinical efficiency. Sparse CBCT scanning is a possible solution to the problems mentioned above but at the cost of inferior image quality. To decrease the extra dose while maintaining the CBCT quality, deep learning (DL) methods are widely adopted. In this study, planning CT was used as prior information, and the corresponding strictly structure-preserved CBCT was simulated based on the attenuation information from the planning CT. We developed a hyper-resolution ultra-sparse-view CBCT reconstruction model, known as the planning CT-based strictly-structure-preserved neural network (PSSP-NET), using a generative adversarial network (GAN). This model utilized clinical CBCT projections with extremely low sampling rates for the rapid reconstruction of high-quality CBCT images, and its clinical performance was evaluated in head-and-neck cancer patients. Our experiments demonstrated enhanced performance and improved reconstruction speed.

Multi-Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) technology can provide users with more flexible and efficient computing services. Most of the existing research works realize multi-UAV collaboration through single-hop or multi-hop task relaying. Although the delay and energy consumption incurred in transmitting mission results to the destination node is relatively small, the UAV node changes dynamically and the result feedback should not be ignored. At the same time, when the remaining power of the UAVs swarms is unevenly distributed, when one UAV runs out of power, the users in the area it serves will fall into congestion again. In order to solve these problems, we propose an energy-aware computation offloading and routing (ECOR) strategy with result feedback. ECOR allows tasks to select the best node by flying ad-hoc network (FANET) multi-hop routing, thus increasing the lifetime of the entire network and the number of tasks completed during the lifetime, i.e., the lifetime value. Meanwhile, we propose to embed the remaining energy and reliability information of the UAV in the Hello message, thus making the UAV energy-aware. Considering the dynamics of the environment and the complexity of the optimization objective, we propose to use Dueling DQN to optimize offloading and routing decisions. After the task is successfully offloaded, the system will enter the result transmission phase to relay the task result to the destination node. Simulation results show that ECOR significantly outperforms other strategies in terms of cumulative rewards, energy efficiency, and task delivery rates.