The rapid evolution of cellular networks, driven by the proliferation of mobile devices and the exponential growth of the Internet of Things (IoT), has significantly advanced wireless communication technologies. Fifth generation of wireless communications technology (5G) enhanced data rates, latency, and network capacity, resulting in the emergence of new applications. However, the sixth generation (6G) is foreseen to support a new set of use cases with diverse requirements. This paper explores the critical role of artificial intelligence (AI) in shaping the trajectory from 5G to 6G. We discuss AI applications in 5G for network planning, resource allocation, traffic management, and security, as well as propose infrastructure upgrades, like edge servers and enhanced network topologies, to support AI in 6G. Additionally, we outline a visionary perspective on AI’s potential contributions to 6G, highlighting its role in enabling innovative services and applications. By providing this forward-looking perspective, this paper aims to stimulate discussion and guide the development of intelligent and autonomous 6G networks.

The integration of Low Earth Orbit (LEO) satellite constellations within 5G and 6G networks offers a promising solution to extend global communication coverage and reduce latency. However, the inherent high velocity and large number of LEO satellites pose significant challenges, particularly in managing complex handover processes that are critical for maintaining seamless connectivity. This paper introduces LEON, a novel system-level simulator developed using OMNeT++ with INET, Simu5G, and OS3 extensions, designed specifically to offer potential solutions to address these challenges. Unlike existing simulators that offer static simulations, it provides a dynamic platform that enables continuous simulation of handover processes using standardized protocols. This capability allows researchers to explore and enhance handover performance in LEO satellite constellations more effectively. LEON simulates the dynamic behaviour of LEO satellites and evaluates handover mechanisms by analyzing channel performance, such as Signal-to-Interference-plus-Noise Ratio (SINR), under various network conditions.

Foreign-related legal talents with firm belief in the rule of law, solid professional foundation, comprehensive practical skills and superb foreign language proficiency are one of the core forces of the "Belt and Road" construction. Based on the methods of empirical analysis, comparative analysis and case analysis, the cultivating path of foreign-related legal talents in local science and technology universities is studied in depth. Taking Xi’an University of Technology as an example, there still exists some issues to be solved, such as the training rules need to be clarified, the construction of the faculty and the practical teaching need to be strengthened. It is necessary to further explore the special training model of foreign-related legal talents in universities of science and engineering, strengthen the construction of high-level teaching staff and enrich foreign-related legal practical teaching methods, in order to meet the requirements of the "Belt and Road" construction for foreign-related legal talents.

With the rapid evolution of programmable network devices, the transformation of network infrastructure to a programmable computing platform has become a reality. As a result, such programmable networks make the deployment of in-network AI possible for achieving predictive maintenance, which in turn enables the much desired (sound-based) time-critical anomaly detection in modern smart factories. However, such abnormal sound detection systems face high computational complexity due to their high density and multi-source nature. In this paper, we present our novel You only hear once (Yoho) design for multi-target anomalous sound detection and end-to-end feature extraction. Yoho can detect anomalies directly from a mixed multi-source acoustic signal without recovering each source data. In addition, with a lightweight dual-path backbone design (Yoho-Dual-Path), we achieve low model complexity and high detection accuracy. Our experiments show that the proposed solution can reduce the inference time by 77% and improve the detection accuracy by 51%. This enables the integration of intelligent anomalous sound detection systems into the network.