构建6G网络的智能协商市场,让各方自动达成最优资源分配。
Agoran: An Agentic Open Marketplace for 6G RAN Automation
- 用三个自主AI分支模拟立法、执行、司法,实现动态决策。
- 实测提升eMBB吞吐37%、URLLC延迟降73%,节省8.3%资源。
- 轻量模型仅需6GiB内存,快速推理,适合部署在边缘设备。
下一代移动网络需协调多方服务方的冲突目标。当前网络切片控制器仍僵化、依赖策略且缺乏商业上下文感知。我们提出Agoran服务与资源代理(SRB),一个类古希腊集市的智能市场,将利益相关方直接引入运行闭环。受古希腊广场启发,Agoran通过三个自治AI分支分配权限:立法分支使用检索增强的大语言模型(LLM)回答合规问题;执行分支通过监听更新的向量数据库保持实时态势感知;司法分支基于规则评估每条代理消息的信任分,同时利用LLM检测恶意行为并实时施加激励以恢复信任。利益相关方侧的谈判代理与SRB侧的调解代理通过多目标优化器生成可行且帕累托最优的提案,在单轮内达成共识意图,并部署至开放和AI RAN控制器。在私有5G测试床上的实验显示,采用真实车辆移动轨迹数据,Agoran实现:(i) eMBB切片吞吐量提升37%;(ii) URLLC切片延迟降低73%;(iii) 端到端减少8.3%的物理资源块(PRB)使用,优于静态基线。一个10亿参数的Llama模型,仅用5分钟在100条GPT-4对话上微调,即可恢复约80% GPT-4.1的决策质量,同时运行在6吉字节内存中,1.3秒内完成收敛。这些结果确立了Agoran作为一条切实可行、符合标准的6G超灵活、利益相关者中心网络路径。现场演示视频见:https://www.youtube.com/watch?v=h7vEyMu2f5w&ab_channel=BubbleRAN。
原文摘要 · Abstract (English)
Next-generation mobile networks must reconcile the often-conflicting goals of multiple service owners. However, today's network slice controllers remain rigid, policy-bound, and unaware of the business context. We introduce Agoran Service and Resource Broker (SRB), an agentic marketplace that brings stakeholders directly into the operational loop. Inspired by the ancient Greek agora, Agoran distributes authority across three autonomous AI branches: a Legislative branch that answers compliance queries using retrieval-augmented Large Language Models (LLMs); an Executive branch that maintains real-time situational awareness through a watcher-updated vector database; and a Judicial branch that evaluates each agent message with a rule-based Trust Score, while arbitrating LLMs detect malicious behavior and apply real-time incentives to restore trust. Stakeholder-side Negotiation Agents and the SRB-side Mediator Agent negotiate feasible, Pareto-optimal offers produced by a multi-objective optimizer, reaching a consensus intent in a single round, which is then deployed to Open and AI RAN controllers. Deployed on a private 5G testbed and evaluated with realistic traces of vehicle mobility, Agoran achieved significant gains: (i) a 37% increase in throughput of eMBB slices, (ii) a 73% reduction in latency of URLLC slices, and concurrently (iii) an end-to-end 8.3% saving in PRB usage compared to a static baseline. An 1B-parameter Llama model, fine-tuned for five minutes on 100 GPT-4 dialogues, recovers approximately 80% of GPT-4.1's decision quality, while operating within 6 GiB of memory and converging in only 1.3 seconds. These results establish Agoran as a concrete, standards-aligned path toward ultra-flexible, stakeholder-centric 6G networks. A live demo is presented https://www.youtube.com/watch?v=h7vEyMu2f5w\&ab_channel=BubbleRAN.
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