APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations
arXiv:2606.11553v1 Announce Type: new Abstract: Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol layers. We present APEX, a network-native, decoder-only transformer for forecasting enterprise AP telemetry, and evaluate it on DHCP degradation as a representative network task. APEX is pre-trained on 10-channel multivariate telemetry from ~4,500 production wireless networks (~100K AP time series, 34 metrics per AP), and is available as APEX-Large (269M, cloud) and APEX-Edge (10.5M, edge). On a
The proliferation of complex, AI-driven networking demands specialized models that overcome the limitations of generic time-series AI for wireless telemetry, necessitating innovations like APEX.
This development indicates a crucial step towards more autonomous and efficient wireless network operations, leveraging AI to improve forecasting and detect anomalies in complex, real-time data.
Specialized, 'network-native' foundation models are emerging for critical infrastructure, moving beyond generic AI applications to address unique challenges in areas like wireless edge operations.
- · Wireless network operators
- · AI infrastructure providers
- · Edge computing platforms
- · Network equipment manufacturers
- · Providers of generic time-series AI models
- · Traditional network monitoring solutions
Improved stability and performance of wireless networks through AI-driven anomaly detection and forecasting.
Accelerated adoption of autonomous operational capabilities in various critical infrastructure sectors.
Enhanced security and resilience of digital communication networks, reducing vulnerabilities to outages and cyber threats.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG