[简介] 随着通信迈入6G时代，传统依赖物理资源扩展的通信模式难以满足智能化、泛在化的发展需求。提出一种意图驱动的智简通信系统，融合认知心理学、信息论与人工智能方法，以语义token为基本单元，构建面向信息效用的通信范式。系统集成智能体的感知、认知与反馈能力，实现异构数据的上下文感知语义建模与压缩传输，重点突破语义编码、意图解析、鲁棒传输与可信解码等关键技术。该架构适配人—人感知、人—机控制与多机协同等差异化需求，支持在带宽受限与信道动态条件下的高效稳健传输。系统梳理了智简通信的研究脉络与核心机制，为构建高效、泛用、可持续的智能通信体系提供理论支撑与技术参考。

[简介]光计算作为一种基于光子进行信息处理的新型计算范式，凭借高并行性、低能耗和大带宽等优势，正成为下一代光网络演进的关键支撑技术。从光计算的架构形式与系统层级双重路径出发，系统解析其技术发展脉络，并归纳关键支撑要素与集成模式。在此基础上，聚焦下一代光网络中的典型应用场景，重点探讨光计算在光信号处理、网络智能优化、通感一体及智算体系等领域的融合路径，揭示其在构建新型信息基础设施中的潜在价值与发展方向。

[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.