A Family of Open Time-Series Foundation Models for the Radio Access Network
This addresses the need for efficient, generalizable AI models in programmable RAN infrastructure, though it is incremental as it builds on existing time-series foundation model concepts.
The paper tackles the problem of model fragmentation and poor generalization in AI-native Radio Access Network (RAN) optimization by introducing TimeRAN, a unified multi-task learning framework that achieves state-of-the-art performance across tasks like anomaly detection and forecasting with minimal fine-tuning, as demonstrated on a 5G testbed.
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.