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.

The operators of 5G networks will wield an unprecedented amount of power, which will have major implications for enterprise digital transformation initiatives.

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.

Productizing data science applications is complex and requires a huge level of expertise in domains from development to integration to run mode. The monolithic application paradigm is no longer valuable nor valid, and Guavus has the best level of skills to help its customers adopt and build reliable micro-services based architectures.

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.

As the COVID-19 pandemic spreads across the world, it’s clear that a new normal has emerged. We view this as an incredible opportunity for CSPs to begin catering to those who work from home. The “collaboration slice” is a service that could be offered by CSPs.

Distributed Machine Learning is a design strategy that caters to solve some of the upcoming challenges in the ever growing field of ML. As data is generated in abundance by Big Data lakes and streaming devices, we need to upgrade the way we do ML and modify it to use in a distributed manner. The challenges in doing so are both mathematical and engineering in nature. This blog tries to address both in details.

Social distancing and self-isolation mean that telecommunications have become an elevated essential service. How will communications service providers (CSPs) meet the challenge of their new critical role in our changed world?