[2604.04271] A Family of Open Time-Series Foundation Models for the Radio Access Network

Computer Science > Networking and Internet Architecture arXiv:2604.04271 (cs) [Submitted on 5 Apr 2026] Title:A Family of Open Time-Series Foundation Models for the Radio Access Network Authors:Ioannis Panitsas, Leandros Tassiulas View a PDF of the paper titled A Family of Open Time-Series Foundation Models for the Radio Access Network, by Ioannis Panitsas and 1 other authors View PDF HTML (experimental) 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 compr...