AI for Network Performance

Radio Frequency (RF) optimization is the most important way to guide CSP investments in coverage and quality to deliver the highest quality of experience (QoE) for customers. To guide their investment decisions, CTOs need a more detailed view of network performance than is possible with conventional drive testing and geolocation tools.

How can CSPs use Spectrum to optimize network efficiency?

Spectrum is one of a CSP’s most valuable resources, as well as one of its most expensive. Making use of finite spectrum is paramount and network investments that focus on the efficient use of spectrum are the most likely to achieve the biggest returns. Conventionally, CSPs have relied on analyzing cells with low average capacity performance indicators at peak traffic times. 

How can Nokia Spectral Performance Analysis improve network data capacity?

A more effective approach is the Nokia Spectral Performance Analysis. Instead of simply analyzing individual cells, the service divides cells into multiple zones with similar characteristics, measures the spectral efficiency for each zone and automatically recommends ways to improve network data capacity. AI makes the analysis more granular, so CAPEX investments can be more precise and bring a higher ROI. 
 

 
Nokia collects, stores and analyzes data from multiple sources, including Minimization of Drive Test (MDT) data. MDT allows performance data to be collected from Nokia and other vendor networks, tapping into billions of anonymized measurement reports sent by ordinary mobile phones.

Optimize RF coverage by identifying coverage gaps and interference, enabling CSPs to prioritize improvement spending.

Hutchinson 3 Indonesia wanted to better understand how its increasingly complex network was performing and make improvements to create a better experience for its subscribers. With its AI-driven solution Nokia delivered the following benefits: