The rapid progress of 6G mobile communication technologies has sparked a great deal research interests. The 6G core network architecture faces formidable challenges due to the escalating complexity of network service demands and diverse application scenarios. In response, our research endeavors to tackle these challenges by proposing a self-evolving architecture based on intelligent decision making. Inspired by the principles of biological morphological evolution, our architecture empowers the core network to dynamically adapt and reshape itself in order to effectively address the evolving communication environments. To facilitate this self-evolutionary process, we introduce a comprehensive framework encompassing mechanisms, architecture, agents, and algorithms that enable the network to autonomously generate and optimize its own structure, thereby ensuring adaptability to a wide range of application scenarios. By conducting concept proof simulation experiments, we have demonstrated the effectiveness of our self-evolution algorithm, which enables the 6G core network to make rational evolving decisions and exhibit remarkable adaptability to various application scenarios.

This research combines a next-generation wireless network and a quadrotor unmanned aerial vehicle (UAV) to create a real-time wireless network quadrotor UAV flight control system. Three major problems will occur when the system operates, such as external disturbances, internal actuator failures and wireless network association failures (random delay and packet loss). We propose a hierarchical distributed comprehensive robust adaptive fault-tolerant control algorithm based on robust fault-tolerant theory to improve the performance of this system. The simulation and flight experimental test results show that when this system is analyzed with respect to external disturbances, internal actuator failures and wireless network association failures, the designed controller stability is asymptotically stable, the performance of this system is very good and the system provides a strategy for establishing a ground-to-air self-organizing wireless network.

We report a wavelength-tunable harmonically mode-locking dissipative soliton fiber laser based on a high-speed intensity modulator with a simple all-polarization-maintaining (all-PM) cavity. The center wavelength can be precisely tuned from 1029.35 to 1079.25 nm by tuning only the frequency of the pulse pattern generator (PPG), and the linear tuning accuracy of the wavelength can reach 0.0093 nm/kHz. The degree of polarization can reach 98.969%. The FWHM of the pulse is measured to be 30.1 ps, and the intracavity pulse energy after the gain fiber can reach 5.41 nJ. All orders of the harmonic mode-locking repetition rate are tuned from $\sim 33.346$ to $\sim 366.806\mathrm{MHz}$ through adjustment of the data length and frequency of PPG.

Low delay performance in VANT need to an intensive study. In this article, we proposed a novel priority method to reduce transmission delay of one-hop in VANET. A priority was marked to each message based on static factors, dynamic factors and size of the message. Messages scheduling was implemented based on the priority of messages. The performance of proposed method was analysed in highway scenario on the average delay and waiting delay in queue. Simulation results are consistent with theoretical deriving. Therefore, by using the novel method, low delay and more reliable communication scenarios can be provided in Vehicular Ad-hoc Network.

With the increasing demand for application development of task publishers (e.g., automobile enterprises) in the Internet of Vehicles (IoV), federated learning (FL) can be used to enable vehicle users (VUs) to conduct local application training without disclosing data. However, the challenges of VUs’ intermittent connectivity, low proactivity, and limited resources are inevitable issues in the process of FL. In this paper, we propose a UAV-assisted FL framework in the context of the IoV. An incentive stage and a training stage are involved in this framework. UAVs serve as central servers, which assist to incentivize VUs, manage VUs’ contributed resources, and provide model aggregation, making sure communication efficiency and mobility enhancement in FL. The numerical results show that, compared with the baseline algorithms, the proposed algorithm reduces energy consumption by 50.3% and improves model convergence speed by 30.6%.

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.