[简介] 围绕低空经济驱动下的广域立体覆盖需求，本研究概述了通信感知一体化（ISAC）在低空智联网中的理论与技术进展。首先，从低空广域立体覆盖需求出发，分析低空场景下通信随机性与感知确定性的内在冲突，引入随机矩阵理论（RMT）确立通感性能的理论边界。随后，结合低空场景传播特性，重点分析多站协同感知、可重构智能表面（RIS）、可移动天线（MA）、空间-极化域联合优化，以及安全、隐私与可信监管等关键技术。本研究旨在为构建高可靠、高精度的智能化低空智联网提供坚实的理论支撑与技术路径。

[简介]无线移动通信覆盖呈现人口密集区域过覆盖、低空立体空域欠覆盖、远域远海无覆盖的非均衡格局。传统蜂窝覆盖已难以满足密集化、立体化及广域按需覆盖需求。本文围绕如何突破移动通信系统平面蜂窝覆盖局限，阐述从信号覆盖到容量覆盖、从平面覆盖到立体覆盖、从固定覆盖到广域按需覆盖的技术途径，探讨密集无线移动网络中的重叠覆盖干扰管控规律、多棱柱立体覆盖结构、卫星系统和地面移动网络融合的广域按需覆盖等关键技术与基本原理，形成了广域立体密集无线移动通信覆盖体系，最后展望了该体系进一步增强的研究方向。

[Introduction] With the rapid advancement of generative artificial intelligence (AI) and large language model (LLM) technologies, AI agents are gradually becoming the core service units in networks, and their communication mode is evolving from local collaboration to wide-area interconnection. The construction of the Internet of Agents (IoA) faces multiple challenges, such as identity management, dynamic networking, and semantic routing, which urgently requires the design of a network protocol system that adapts to its new traffic characteristics and collaboration needs. Based on the application scenarios of agent communication, this paper systematically analyzes the management, control, and routing requirements that multi-agent collaboration imposes on IP networks, proposes a three-layer functional architecture for the IoA, and designs a protocol suite covering management, control, and routing around key issues such as agent registration and identification, service discovery, capability sensing, and cross-domain traffic assurance. By extending existing Internet protocols and introducing a semantically aware routing mechanism, this paper provides a scalable, efficient, and secure approach to implementing a protocol for end-to-end agent collaboration, thereby contributing to the construction of an open, large-scale agent collaboration ecosystem.

[Introduction] The convergence of artificial intelligence (AI) with the physical world is reshaping the future of intelligent systems through real-time perception, interaction, and control within physical environments. To support this new paradigm, 6G networks are envisioned as critical enablers, offering ultra-low latency, high reliability, and service-aware intelligence to facilitate seamless human-machine collaboration. This paper proposes a functional framework that integrates Agentic AI into the 6G architecture, introducing the concept of Agentic AI-Enabled 6G Network Services (AA6NS). In this framework, user intents are translated and processed across the application layer, core network (CN), and radio access network (RAN), where Agentic AI dynamically manages task-level quality of service/quality of experience (QoS/QoE), orchestrates multi-device service groups, and enables real-time network adaptation. The proposed architecture with the new 6G techniques establishes a foundation for future physical AI applications across domains such as autonomous mobility, smart manufacturing, and remote robotics.