The Radio Access Network (RAN) is evolving into a programmable and disaggregated infrastructure that increasingly relies on AI-native algorithms for optimization and closed-loop control. However, current RAN intelligence is still largely built from task-specific models tailored to individual functions, resulting in model fragmentation, limited knowledge sharing across tasks, poor generalization, and increased system complexity. To address these limitations, we introduce TimeRAN, a unified multi-task learning framework for time-series modeling in the RAN. TimeRAN leverages a lightweight time-series foundation model with few task-specific heads to learn transferable representations that can be efficiently adapted across diverse tasks with limited supervision. To enable large-scale pretraining, we further curate and open-source TimeRAN DataPile, the largest time-series corpus for RAN analytics to date, comprising over 355K time series and 0.56B measurements across diverse telemetry sources, protocol layers, and deployment scenarios. We evaluate TimeRAN across a comprehensive set of RAN analytics tasks, including anomaly detection, classification, forecasting, and imputation, and show that it achieves state-of-the-art performance with minimal or no task-specific fine-tuning. Finally, we integrate TimeRAN into a proof-of-concept 5G testbed and demonstrate that it operates efficiently with limited resource requirements in real-world scenarios.

翻译：无线接入网（RAN）正演变为一种可编程、可分解的基础设施，并日益依赖AI原生算法进行优化和闭环控制。然而，当前的无线接入网智能主要仍由针对各个功能量身定制的任务特定模型构建，这导致模型碎片化、跨任务知识共享受限、泛化能力差以及系统复杂度增加。为应对这些局限，我们提出了TimeRAN——一个用于无线接入网时间序列建模的统一多任务学习框架。TimeRAN利用轻量级时间序列基础模型，并辅以少量任务特定的头部网络，学习可迁移的表征，这些表征能够在有限监督下高效地适应各种任务。为实现大规模预训练，我们进一步整理并开源了TimeRAN DataPile，这是迄今为止最大的用于无线接入网分析的时间序列语料库，包含来自不同遥测源、协议层和部署场景的超过35.5万个时间序列和5.6亿个测量数据。我们在广泛的无线接入网分析任务集上（包括异常检测、分类、预测和插补）评估了TimeRAN，结果表明，即便任务特定微调极少甚至不做微调，它也能达到最先进的性能。最后，我们将TimeRAN集成到一个概念验证的5G测试平台中，并展示了它在实际场景中能以有限的资源需求高效运行。