[简介] 数字化时代下，云网融合推动网络向虚拟化、服务化深度演进，传统基于边界防护的被动安全体系难以满足远程办公、智能运维等新兴场景的需求。聚焦新一代电信云网架构演进趋势，系统梳理业界内生安全技术路线，剖析其面临的云网环境下安全可见性不足、安全能力与云网业务协同滞后等挑战，提出一种深度嵌入云网基础设施的内生安全架构。该架构涵盖智能威胁感知、零信任策略联动等关键技术。本研究可为电信云网的安全范式转型及产业实践提供理论支撑与技术参考。

[简介]安全是保障网络稳定可靠的基石，在构建下一代移动通信网络的过程中，设计全面且先进的6G安全架构至关重要。分析了移动通信网安全演进规律和6G网络安全新需求，总结归纳对于6G网络安全架构设计的启示，系统性设计了6G网络的安全域模型和安全架构，为后续进一步深入探讨6G安全关键技术、推动6G安全产业发展提供指引和参考。

[Introduction] Reliable channel data help characterize the limitations and performance boundaries of communication technologies accurately. However, channel measurement is highly costly and time-consuming, and taking actual measurement as the only channel data source may reduce efficiency because of the constraints of high testing difficulty and limited data volume. Although existing standard channel models can generate channel data, their authenticity and diversity cannot be guaranteed. To address this, we use deep learning methods to learn the attributes of limited measured data and propose a generative model based on generative adversarial networks to rapidly synthesize data. A software simulation platform is also established to verify that the proposed model can generate data that are statistically similar to the measured data while maintaining necessary randomness. The proposed algorithm and platform can be applied to channel data enhancement and serve channel modeling and algorithm evaluation applications with urgent needs for data.

[Introduction] The advent of 6G wireless networks promises unprecedented connectivity, supporting ultra-high data rates, low latency, and massive device connectivity. However, these ambitious goals introduce significant challenges, particularly in channel estimation due to complex and dynamic propagation environments. This paper explores the concept of channel knowledge maps (CKMs) as a solution to these challenges. CKMs enable environment-aware communications by providing location-specific channel information, reducing reliance on real-time pilot measurements. We categorize CKM construction techniques into measurement-based, model-based, and hybrid methods, and examine their key applications in integrated sensing and communication systems, beamforming, trajectory optimization of unmanned aerial vehicles, base station placement, and resource allocation. Furthermore, we discuss open challenges and propose future research directions to enhance the robustness, accuracy, and scalability of CKM-based systems in the evolving 6G landscape.