arXiv:2606.19821v1 Announce Type: cross Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, existing machine learning (ML) approaches face significant limitations in scalability and explainability, restricting their effectiveness in real-world deployments. We propose TelcoAgent, a foundation model-based framework that enables accurate, scalable, and explainable forecasting of multiple KPMs across diverse network cells without the need for site-specific training. Specifically, the framework co
The increasing complexity of 5G and next-generation networks necessitates more sophisticated, scalable, and explainable AI for proactive management, pushing the development of foundation model-based solutions.
This development allows telecommunication companies to manage vast, distributed networks more efficiently and preemptively, ensuring service quality and reducing operational costs through advanced AI applications.
Network management shifts from reactive to proactive, with AI foundation models providing comprehensive, explainable forecasting across diverse network segments without extensive site-specific training.
- · Telecommunication companies
- · AI infrastructure providers
- · 5G equipment manufacturers
- · Legacy network management software vendors
- · Human-centric network operations teams
Improved network reliability and reduced downtime for 5G consumers and businesses.
Accelerated adoption of more advanced 5G applications due to better foundational network stability.
Resource optimization in telecommunications leading to potential re-allocation of capital towards further network expansion or new service development.
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