ASTRA：面向下一代移动通信中智能设备-网络-云协同的智能体AI

中文摘要 AI 辅助

向下一代移动通信系统的演进要求网络具备智能原生能力，能够自主适应用户意图，然而当前基于3GPP协议驱动的设备-网络-云（DNC）架构存在三个结构性瓶颈：协议约束的决策空间将优化限制在预定义参数子集内；有损接口压缩导致的级联信息不对称，剥离了语义上下文并引发意图校准错误；以及固有的反应式协调机制，仅在性能退化后才触发行动。本文提出了一种名为电信资源自主智能体协同（ASTRA）的自主智能体AI范式，引入三层智能体层，包括设备智能体、网络智能体和云智能体，将网络智能与底层硬件基础设施解耦。这些智能体通过双向语义通道协作，包括语义意图消息、能力抽象消息、全局指令和对等协调，执行感知、推理与预测、通信、决策、行动和学习六个阶段的循环，将反应式协议驱动操作转变为在全决策空间上的主动、意图校准的优化。通过在两个代表性场景中的系统级仿真验证，ASTRA在密集人群小区选择中通过语义负载交换重新分配拥塞小区的用户，实现了13.1%的平均吞吐量提升，并在高速移动场景中通过预测性轨迹感知协调减少了18.2%的被动切换，初步证据表明所提出的智能体框架能够访问在协议约束架构下结构上无法触及的解决方案区域。

英文摘要

The evolution toward next-generation mobile communication systems demands intelligence-native networks capable of autonomously adapting to user intent, yet the prevailing 3GPP protocol-driven device-network-cloud (DNC) architecture imposes three structural bottlenecks: protocol-constrained decision spaces confining optimization to predefined parameter subsets, cascaded information asymmetry from lossy interface compression that strips semantic context and causes intent miscalibration, and inherently reactive coordination mechanisms that trigger actions only after performance degradation. This paper proposes an autonomous agentic AI paradigm named Agentic Synergy for Telecommunication Resource Autonomy (ASTRA), which introduces a three-tier agent layer, including device agent, network agent, and cloud agent, decoupling network intelligence from the underlying hardware infrastructure. These agents collaborate through bidirectional semantic channels, including semantic intent messages, capability abstraction messages, global directives, and peer coordination, executing a six-phase cycle of perceive, reason and predict, communicate, decide, act, and learn that transforms reactive protocol-driven operations into proactive, intent-calibrated optimization over the full decision space. Validated through system-level simulations in two representative scenarios, ASTRA achieves a 13.1\% average throughput gain in dense-crowd cell selection by redistributing UEs from congested cells via semantic load exchange, and an 18.2\% passive handover reduction in high-speed mobility through predictive trajectory-aware coordination, providing initial evidence that the proposed agentic framework accesses solution regions structurally inaccessible under protocol-constrained architectures.

发表机构

• Tsinghua University（清华大学）
• Huawei Technologies Co. Ltd（华为技术有限公司）

机构由 AI 辅助整理，请以论文原文为准。