A Family of Open Time-Series Foundation Models for the Radio Access Network

• URL: http://arxiv.org/abs/2604.04271v1
• Date: Sun, 05 Apr 2026 21:24:04 GMT
• Title: A Family of Open Time-Series Foundation Models for the Radio Access Network
• Abstract summary: TimeRAN is a unified multi-task learning framework for time-series modeling in the RAN.<n>TimeRAN learns transferable representations that can be efficiently adapted across diverse tasks with limited supervision.<n>We evaluate TimeRAN across a comprehensive set of RAN analytics tasks, including anomaly detection, classification, forecasting, and imputation.
• Score: 7.440677492469226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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