arXiv:2606.06663v1 Announce Type: new Abstract: Future AI-integrated Radio Access Networks (AI-RAN) will combine open programmability with learning-enabled xApps, rApps, and control functions that act on shared parameters and key performance indicators (KPIs). For conflict monitoring, it is not enough to know which applications are deployed; the system must also know whether the parameter--KPI dependencies assumed by runtime diagnosis remain valid under the current operating regime. This paper studies a lightweight monitoring primitive for that purpose: tracking an interpretable dependency rep
The increasing complexity and integration of AI into critical infrastructure like RANs necessitates advanced monitoring solutions to ensure stability and reliability.
This work addresses a core challenge in deploying AI safely and effectively in real-world systems, preventing conflicts and ensuring predictable performance in next-generation networks.
The ability to track and explain runtime dependencies in AI-RANs improves conflict monitoring, moving towards more robust and transparent AI-integrated networks.
- · Telecommunications companies
- · AI-RAN developers
- · Network infrastructure providers
- · Critical infrastructure operators
- · Legacy network monitoring solutions
- · Vendors without explainable AI capabilities
Improved stability and reliability of AI-driven telecommunications networks.
Accelerated adoption of AI in other critical infrastructure due to enhanced monitoring capabilities.
New regulatory frameworks for explainable AI in safety-critical systems emerge globally.
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