arXiv:2512.01035cs.NIcs.AI2025-12被引 3

让网络理解任务目标,实现智能协同的6G新架构

Goal-Oriented Multi-Agent Semantic Networking: Unifying Intents, Semantics, and Intelligence

论文配图:Goal-Oriented Multi-Agent Semantic Networking: Unifying Intents, Semantics, and Intelligence
图 1 · 摘自论文原文
  • 用多个智能代理协同完成感知、通信、计算与控制
  • 在机器人故障检测中提升任务成功率72%,节能99%
  • 适合需要智能协同与能效优化的6G应用

6G服务正朝着以目标为导向和人工智能原生的方向演进,有望在各行业带来变革性社会效益并促进能源可持续。然而,现有网络架构因应用与网络完全解耦,无法暴露或利用高层目标,限制了对服务需求的智能适应能力。本文提出面向目标的多智能体语义网络(GoAgentNet),将通信从数据交换提升至目标达成。该架构通过将应用与网络功能抽象为多个协作智能体,借助语义计算与跨层语义网络,联合编排多智能体感知、通信、计算与控制,使整个系统共同追求统一的应用目标。我们首先分析传统网络设计在支持6G服务中的局限性,并指出GoAgentNet的关键使能技术。随后,通过三个典型6G应用场景展示其如何实现更高效、更智能的服务。进一步识别出部署挑战并提出潜在解决方案。一项关于机器人故障检测与恢复的案例研究显示,相比现有无GoAgentNet的架构,本方案可提升任务成功率最高达72%,能耗降低最高达99%,凸显其在支持可扩展、可持续6G系统方面的潜力。

原文摘要 · Abstract (English)

6G services are evolving toward goal-oriented and AI-native communication, which are expected to deliver transformative societal benefits across various industries and promote energy sustainability. Yet today's networking architectures, built on complete decoupling of the applications and the network, cannot expose or exploit high-level goals, limiting their ability to adapt intelligently to service needs. This work introduces Goal-Oriented Multi-Agent Semantic Networking (GoAgentNet), a new architecture that elevates communication from data exchange to goal fulfilment. GoAgentNet enables applications and the network to collaborate by abstracting their functions into multiple collaborative agents, and jointly orchestrates multi-agent sensing, networking, computation, and control through semantic computation and cross-layer semantic networking, allowing the entire architecture to pursue unified application goals. We first outline the limitations of legacy network designs in supporting 6G services, based on which we highlight key enablers of our GoAgentNet design. Then, through three representative 6G usage scenarios, we demonstrate how GoAgentNet can unlock more efficient and intelligent services. We further identify unique challenges faced by GoAgentNet deployment and corresponding potential solutions. A case study on robotic fault detection and recovery shows that our GoAgentNet architecture improves energy efficiency by up to 99% and increases the task success rate by up to 72%, compared with the existing networking architectures without GoAgentNet, which underscores its potential to support scalable and sustainable 6G systems.

6G网络多智能体语义通信能效优化

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