About the Client
The client is a Tier-1 CSP in Europe
Challenge
Despite mature network monitoring systems, the CSP’s RAN operations team largely operated in a post‑impact mode only after service degradation had already affected users.
Key challenges included:
- Delayed detection: Cell accessibility issues were identified only after users were already affected.
- Manual diagnostics: Root cause analysis heavily relied on manual workflows, slowing response.
- Operational overload: Engineering teams spent significant time firefighting rather than preventing incidents.
- Lack of prioritization: Without early risk indicators, resources were spread across the network instead of focused on the most vulnerable cells.
Visibility After Impact was Too Late. The CSP needed a way to see risk earlier, act faster, and reduce customer impact without increasing operational complexity.
Solution
Predictive Intelligence Embedded. To address these challenges, TCTS intervened with a targeted proof of concept using its AI–based predictive intelligence capability purpose‑built for cell accessibility.
What TCTS Delivered
- Custom AI model development
TCTS developed and trained machine learning models using historical RAN performance management data to identify early indicators of cell inaccessibility. - Predictive risk visibility
The AI models classified cells based on their likelihood of becoming inaccessible, providing up to 24 hours of advance warning. - Actionable, cell‑level insights
Predictions were delivered at a granularity that allowed network teams to:- Prioritize high‑risk cells
- Take targeted preventive action
- Reduce reliance on broad, manual troubleshooting
- Continuous learning
TCTS ensured periodic model improvement using new data, allowing the model to learn from newly observed network behaviour and improve prediction accuracy over time.
By leveraging predictive analytics, the CSP shifted from a reactive to a predict‑and‑prevent approach, without disrupting existing RAN operations.
Results
The successful POC delivered measurable improvements, validating the solution’s potential for a full-scale deployment
Performance Enhancement
- 87% accuracy in predicting inaccessible cells
- The model predicted high-risk cells up to 24 hours in advance
- Up to 25% fewer incidents that impact customers
Operational Improvement
- Reduced reliance on reactive “war-room” troubleshooting.
- Faster isolation of likely root causes at the first level.
- Improved engineering productivity through focused, risk-based workflows.
The PoC demonstrated how expanded data ingestion could further enhance model performance and support long-term RAN robustness at scale.
The success of the PoC provided the client with the confidence to
Consistently higher cell accessibility as potential degradations is anticipated early, keeping network entry success rates close to normal even under impaired conditions.
A smoother, more reliable user experience with fewer attach failures and minimal call or data setup issues, driving measurable improvements in NPS.
Greater subscriber loyalty and lower churn as persistent service unavailability, even in covered areas, is systematically eliminated.
Carry more traffic in peak hours as cells remain accessible when demand is highest, reducing dropped sessions, retries, and abandoned data usage, protecting revenue.
Lower RAN operating costs through proactive network interventions that reduce reactive troubleshooting, repeated site visits, and prolonged accessibility fault resolution.
