Case Study
Automated ML framework enhances network quality and productivity by over 20% for global telco
OUR CLIENT
As a global telecommunications leader with operations in over 100 countries, our client provides a range of services, including network infrastructure, technology solutions, and advanced telecommunications equipment, to telecommunications providers, enterprises, and public sector organizations around the world.
THE CHALLENGE
Managing high-volume network optimization—Streamlining cell upgrades and quality audits
The client struggled to manage extensive network optimizations, including monthly software upgrades and parameter audits for 50,000 cells. Of critical importance, the company wanted to identify and resolve sleeping cells, a situation where there’s poor network performance, but there’s no alarm notification to indicate an issue. Recognizing the inefficiencies of managing frequent software releases, high-volume network audits, and large-scale data analysis, the client sought automations to streamline software upgrades, detect sleeping cells, and conduct timely audits.
THE TRANSFORMATION
Enhancing network quality and efficiency with advanced Python tools and VBA automation for network parameter analysis
UST developed a machine-learning (ML) framework using specialized Python tools to automatically detect sleeping cells, identify underperforming units, and recommend remediation actions. The solution provided detailed network audit reporting, pinpointing root causes, such as configuration issues, hardware faults, or interference. Additionally, UST optimized network parameters, including power levels and frequency allocations, to enhance performance and cell utilization. Meanwhile, Microsoft Office VBA (Visual Basic for Applications) macro network parameter analysis swiftly identified and corrected underutilized or inactive cells.
Post-implementation, the client expanded its team to efficiently manage larger cell optimization volumes and address sleeping cells to ensure optimal network service quality. These initiatives significantly reduced network audit cycle times and accelerated issue resolution, boosting overall operational efficiency.
THE IMPACT
Sleeping cell detection framework enhanced productivity by more than 20%
UST's innovative solution helped the telecom company achieve these benefits:
- Reduced network audit cycle times by 35%—after addressing and resolving sleeping cell issues and efficiently managing larger cell optimization volumes
- Increased productivity by 15%—thanks to UST's specialized Python tools that accelerated sleeping cells detection
- Realized an additional 20% productivity gain—by using the VBA tool to quickly correct degraded network parameters and improve KPIs
- Improved network quality by 7%—by resolving network issues faster
Ready to transform your telecommunications network quality, reliability, and operational efficiency? Learn more about how we leverage cutting-edge tools, like Python and automations, to design and optimize networks for today’s increasingly connected and competitive telecom landscape.