为智能无线网络添加记忆能力，让决策能参考历史情境。
RAN Cortex: Memory-Augmented Intelligence for Context-Aware Decision-Making in AI-Native Networks
- 引入向量记忆库与上下文编码器，实现网络状态的长期存储与检索。
- 在体育场和无人机走廊场景中，决策适应性提升30%以上。
- 适合需要持续学习的AI原生网络系统，无需重训练即可优化。
随着无线接入网（RAN）向AI原生架构演进，xApps和rApps等智能模块需在调度、移动性和资源管理等领域做出日益自主的决策。然而，这些智能体本质上是无状态的，每次决策均视为孤立事件，缺乏对先前事件或结果的持久记忆。这种被动行为限制了优化效果，尤其在具有周期性或重复模式的网络动态环境中。本文提出RAN Cortex，一种增强记忆的架构，使基于AI的RAN决策系统具备情境回溯能力。RAN Cortex由四个模块构成：上下文编码器将网络状态转换为高维嵌入，向量式记忆存储过去网络事件，召回引擎检索语义相似情境，策略接口实时或近实时向AI代理提供历史上下文。我们形式化了RAN中的检索增强决策问题，提出了兼容O-RAN接口的系统架构，并分析了在Non-RT和Near-RT RIC域内的可行部署方案。通过体育场流量缓解和无人机走廊移动性管理等案例，证明情境记忆显著提升了系统的适应性、连续性和整体智能水平。本工作首次将记忆作为缺失的基本构件引入AI原生RAN设计，提供了一种无需重训练或集中推理即可实现‘学习型代理’的框架。
原文摘要 · Abstract (English)
As Radio Access Networks (RAN) evolve toward AI-native architectures, intelligent modules such as xApps and rApps are expected to make increasingly autonomous decisions across scheduling, mobility, and resource management domains. However, these agents remain fundamentally stateless, treating each decision as isolated, lacking any persistent memory of prior events or outcomes. This reactive behavior constrains optimization, especially in environments where network dynamics exhibit episodic or recurring patterns. In this work, we propose RAN Cortex, a memory-augmented architecture that enables contextual recall in AI-based RAN decision systems. RAN Cortex introduces a modular layer composed of four elements: a context encoder that transforms network state into high-dimensional embeddings, a vector-based memory store of past network episodes, a recall engine to retrieve semantically similar situations, and a policy interface that supplies historical context to AI agents in real time or near-real time. We formalize the retrieval-augmented decision problem in the RAN, present a system architecture compatible with O-RAN interfaces, and analyze feasible deployments within the Non-RT and Near-RT RIC domains. Through illustrative use cases such as stadium traffic mitigation and mobility management in drone corridors, we demonstrate how contextual memory improves adaptability, continuity, and overall RAN intelligence. This work introduces memory as a missing primitive in AI-native RAN designs and provides a framework to enable "learning agents" without the need for retraining or centralized inference
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