Nowadays, a large number of civilian unmanned aerial vehicles (UAVs) are increasingly being used in many of our daily applications. However, there are many kinds of UAVs that need to enhance their endurance due to their limited resources. In order to make the UAVs operate efficiently, it is necessary to schedule UAVs with charging requirements. In this paper, renewable energy production and storage equipment on the basis of traditional charging stations is adopted to reduce the power purchase from the distribution network as much as possible. An online algorithm based on Lyapunov optimization is proposed to schedule the charging of UAVs and the energy management of the charging station. Meanwhile, contract theory is used to design the optimal charging strategy in the case of information asymmetry. Hence, local renewable energy can be utilized to the greatest extent, and electricity purchase costs can be reduced. Through the incentive system, users can spontaneously charge at low peak times and avoid the risk of grid overload and high energy cost. The simulation results show that the algorithm can improve the efficiency of charging station operators, allowing users to avoid charging at peak times, and only use real-time information to schedule UAVs. Compared to other algorithms, the proposed scheme can bring good revenue to operators while ensuring long-lasting operations of charging stations.

In this letter, we propose to apply rate-splitting multiple access (RSMA) to improve the system’s spectral efficiency (SE) and manage the severe multi-user interference in the mixed near- and far-field communication system. Specifically, RSMA is utilized to serve all near-field and far-field users on the same time-frequency resource. To further explore the potential of RSMA, we consider the user selection to design the optimal rate-splitting strategy. To this end, we formulate an optimization problem to maximize the sum SE via the joint consideration of user selection, hybrid precoding, and rate allocation. For tackling this non-convex problem, we propose an alternative optimization algorithm based on successive convex approximation and simulated annealing methods. Simulation results reveal that our proposed algorithm achieves a larger performance gain than the other competing benchmark schemes in terms of SE improvement.

The continuous development of smart industrial parks has imposed increasingly stringent requirements on safety helmet detection in environments such as factories, construction sites, rail transit, and fire protection. Current models often suffer from issues like false alarms or missed detections, especially when dealing with small and densely packed targets. This study aims to enhance the YOLOv5 target detection method to provide real-time alerts for individuals not wearing safety helmets in complex scenarios. Our approach involves incorporating the ECA channel attention mechanism into the YOLOv5 backbone network, allowing for efficient feature extraction while reducing computational load. We adopt a weighted bi-directional feature pyramid network structure (BiFPN) to facilitate effective feature fusion and cross-scale information transmission. Additionally, the introduction of a decoupling head in YOLOv5 improves detection performance and convergence rate. The experimental results demonstrate a substantial improvement in the YOLOv5 model's performance. The enhanced YOLOv5 model achieved an average accuracy of 95.9% on a custom-made helmet dataset, a 3.0 percentage point increase compared to the original YOLOv5 model. This study holds significant implications for enhancing the accuracy and robustness of helmet-wearing detection in various settings.

6G networks can flexibly arrange network resources in multiple areas through integration with edge computing and cloud computing. However, devices at the edge of the network generate large quantities of multimodal data (e.g., user behaviour records, audio, and video), creating new challenges for data security and privacy, which has led to research and practical applications of privacy protection methods based on information hiding. In this article, we propose an information hiding method based on multidimensional feature fusion for privacy protection in 6G networks. This method is based on the strong attack resistance of carriers carrying private information; we study multidimensional feature fusion arbitration methods and construct an efficient and accurate feature-based information hiding technology. In this paper, we first perform private information preprocessing to encrypt private information and make it more secure. Then, we perform multidimensional feature extraction on the carrier and fuse these features to obtain the final feature region. Finally, the step of hiding or extracting private information is performed. A large number of experiments show that the performance of the proposed method is superior to those of existing methods.