用智能体+语义协议，让月球无线网络自动适应环境变化。
Agentic Semantic Control for Autonomous Wireless Space Networks: Extending Space-O-RAN with MCP-Driven Distributed Intelligence
- 引入语义智能体与MCP协议，实现跨层自主决策。
- 支持实时、近实时、非实时控制层的上下文感知响应。
- 适合月球探测等高动态复杂环境下的通信系统设计。
月球表面作业对无线通信系统提出严苛要求，包括自主性、抗干扰能力及对环境和任务驱动上下文的适应能力。尽管Space-O-RAN提供了符合3GPP标准的分布式编排架构，但其决策逻辑仅限于静态策略，缺乏语义集成。本文提出一种新扩展，通过模型上下文协议（MCP）与智能体间（A2A）通信协议构建语义智能体层，实现跨实时、近实时与非实时控制层的上下文感知决策。部署在漫游车、着陆器和月球基地站的分布式认知智能体，实施无线感知协调策略，如延迟自适应推理和带宽感知语义压缩，并与多个MCP服务器交互，综合遥测数据、移动规划与任务约束进行推理。
原文摘要 · Abstract (English)
Lunar surface operations impose stringent requirements on wireless communication systems, including autonomy, robustness to disruption, and the ability to adapt to environmental and mission-driven context. While Space-O-RAN provides a distributed orchestration model aligned with 3GPP standards, its decision logic is limited to static policies and lacks semantic integration. We propose a novel extension incorporating a semantic agentic layer enabled by the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication protocols, allowing context-aware decision making across real-time, near-real-time, and non-real-time control layers. Distributed cognitive agents deployed in rovers, landers, and lunar base stations implement wireless-aware coordination strategies, including delay-adaptive reasoning and bandwidth-aware semantic compression, while interacting with multiple MCP servers to reason over telemetry, locomotion planning, and mission constraints.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。