Guavus Blog

High quality in mobile voice services is table stakes and MNOs must proactively and adequately equip themselves for the transition from 4G to 5G.

No matter how sophisticated smartphone devices get, they still need to initiate, connect and complete telephone calls flawlessly.

Actionable device intelligence is essential for MNOs to both understand subscriber behavior when connected to the network and to maximize the effectiveness of device promotion campaigns.

A limited understanding of device intelligence is a handicap for executing on customer-centric business and operations strategies.

Zero-touch networks are the vision for 5G, but the adoption of standard enabling technologies will be key for mobile operators to realize the power of self-driving 5G networks.

While autonomous networks are the ultimate goal in 5G, until we get there, the intelligence generated by NWDAF is critical for human decision making in semi-automated network operations. Let’s take a look at the NWDAF use cases considered by the 3GPP.

A standard approach to 5G network automation will allow Mobile Network Operators (MNOs) to deploy multi-vendor networks with confidence, leveraging the unique capabilities of best-of-breed products.

Disaggregated cloud native 5G infrastructure will enable Mobile Network Operators (MNOs) to deploy best-of-breed products, but this requires an open NWDAF to ensure multi-vendor network automation.

While 5G networks are inherently complex and difficult to manage at scale, AI/ML will power 5G networks that are self-monitoring and self-driving, enabling zero-touch network automation.

There’s a common pain point among communications service providers – how to integrate analytics into the network, currently the analytics are complex from the many non-standardized interfaces and inconsistent data collection techniques across network vendors. These concerns may finally be addressed by the network data analytics function (NWDAF).

For CSPs, understanding how their network is utilized by their subscribers and how the subscribers experience the network and services are the significant factors influencing their revenue and business value. Guavus, today announced Guavus-IQ, a comprehensive product portfolio that provides a multi-perspective analytics experience for CSPs.

This article is an excerpt from the Tractica Research Report on Artificial Intelligence for Telecommunications Applications, published in 3Q 2019. This Tractica report details the major market drivers and barriers, technologies, key players, and forecasts related to eight telecom AI use cases.

Most CSPs know they need to harness their valuable data, but struggle with how to do it. Putting AI and analytics tools to work alongside their deep-packet inspection (DPI) classification engines, providers can learn a lot more about their customers, then categorize them in understandable ways that allow the provider’s marketing department to approach them with targeted, personalized and meaningful service offers. Let’s examine a few ways CSPs can succeed.

5G promises both tremendous opportunity and challenges. Leveraging AI and analytics, CSPs can take the volume of data generated by the network and turn it into useful, consumable information that helps them grow their business and increase their top line.

Through machine learning techniques CSPs are able to reduce alarm volume by more than 90%, while providing increased visibility into customer-impacting issues developing in their network.

Guavus analytics experts explain how applying root issue analysis will drastically reduce the number of network alarms, reveal the hidden structure of issues and clearly identify the strength of the relationships between the issues.