Machine learning based network optimization

Mobile network operators worldwide face cost pressures and escalating network complexity. The advent of 5G-NR has ushered in new use cases and flexibility, but it also comes with more stringent performance and availability requirements.

Machine Learning can serve as a catalyst for the market to unearth deep insights that would otherwise remain concealed. It can also significantly streamline everyday tasks by fostering a smarter system that guides users through their routine work processes, rather than obliging them to repeat each (manual) step. Rohde & Schwarz Mobile Network Testing has focused its efforts on delivering Machine Learning use cases that distill relevant insights from drive testing data, thereby offering substantial benefits to users.

This educational note explains the motivation and reasoning why this kind of approach is needed, what are the benefits of the machine learning offering and it takes a deeper look into how it can be used in various levels of statistical and technical analysis. Finally, this document provides real measurement results and analysis findings based on the Rohde & Schwarz post processing SW suite SmartAnalytics and its machine learning based features Call Stability Score and Network Utilization Rating.