RAN Cortex: Memory-Augmented Intelligence for Context-Aware Decision-Making in AI-Native Networks
Sebastian Barros
TL;DR
RAN Cortex addresses the stateless limitation of AI-native RAN decision-making by introducing a memory-augmented retrieval layer that grounds current actions in semantically similar past episodes. The architecture comprises a Context Encoder, Vector Memory Store, Recall Engine, and Policy Interface, enabling retrieval-augmented decisions without retraining and with compatibility to O-RAN interfaces. It formalizes the retrieval process, defines latency and integration requirements, and outlines a practical deployment path with inline and asynchronous execution modes, safety fallbacks, and a broad validation roadmap. The approach promises improved mobility management, admission control, beamforming, and anomaly handling by increasing sample efficiency, generalization, and decision continuity, offering a scalable path toward intelligent, context-aware RAN control in 5G and beyond. In short, RAN Cortex turns memory into a practical primitive for AI-native networks, enabling learning-enabled agents to operate with memory without compromising safety or interoperability.
Abstract
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