What is the relationship between AI and 5G? – Ericsson

The impact of 5G

The commercial roll out of 5G is now under way. But simply put, 5G is not just another G. Its a complete ecosystem change in the way networks are run and managed, including how applications run on the network.

There are three main use case groups in 5G:

Other, emerging use case groups include massive machine type communication, or MTC.

This is where the connectivity and density of 5G really comes into play.

MTC enables the connectivity of a huge number of devices millions, billions of devices in fact, all of which are connected. Although theyre more likely to send very low data rates, the number of devices, and their long battery life means they can open the doors to brand new industrial use cases. For example, monitoring, farming, agriculture, transportation, automotive, smart cities, and healthcare could all transform thanks to MTC. Its all about connecting human expertise to a huge number of connected sensors for faster, more efficient insights.

Another emerging technology is ultra-reliable, low latency communications, or URLLC. This is where 5G shines. Use cases with URLLC can deliver very low latencies, down to one millisecond, which is a perfect solution for mission-critical use cases from vehicle to vehicle, remote diagnostics, or remote surgery.

URLLCs low latency is perfect for mission-critical use cases such as those in healthcare.

When it comes to 5G networks, AI is no longer a nice to have, but a must-have component to tackle the tremendous complexity that comes with 5G. AI along with the data and automation capabilities that come with it supports the diverse ecosystem of evolving networks in a way that humans alone are unable to manage.

The expectations of 5G are high due to its potential to transform industries. Service providers expect high performance, low latency, throughput and availability that 5G promises. As a result, the ability to operate 5G networks will need to speed up in fact, the development of high-level operational capabilities like zero-touch and self-healing networks are already in the works to meet this growing demand.

The evolution of networks involves some tough challenges, the first of which is data, particularly, how to shape network operations to be data-centric and data-driven. For example, the data elements within a 5G network are highly distributed. It comes in all shapes, sizes, and volumes. So how is it possible to efficiently manage this data? After all, data is what drives capabilities like machine learning and advanced analytics. Without it, we cant run future networks.

First, a clearly defined and executed data-driven strategy is crucial for service providers; one that drives how data should be managed across operations end-to-end, from ingestion all the way to final decision making.

Second, clear decisions need to be made around where and how data is processed, so AI logic can make timely decisions. For example, data could be transferred to a centralized cloud location to be processed for AI inference, but that may incur high transfer costs and additional delays especially for real-time use cases where decisions must be made in a split second. Instead, AI inference could be moved closer to the data source as well as creating a shorter and leaner pipeline.

Another important aspect is to ensure data quality and lineage, end-to-end, so decisions can be made based on trustworthy and high-quality data input. It makes no sense to rely on an AI logic if the data is corrupt.

And finally, organizational transitions regarding competence, technology development, and future-proofing employees skills are all additional challenges that can occur with 5G and AI adoption.

To overcome these new challenges, Ericsson changed its approach from being reactive to becoming much more proactive and predictive, which is the baseline of our AI modeling. The result is a model called the Ericsson Operations Engine. In parallel to our data-driven approach, were also upskilling our people who can see the network from an end-to-end perspective.

We also focus on data analytics, competency development, and 5G technologies, along with developing specific use case experts to support new industry requirements. We need to have the competence to understand the whole ecosystem of these emerging use cases not just an understanding of the technology, but also the various platforms and tools to help run network operations in a smoother and more automated way.

As service providers begin to offer 5G services to enterprises, the efficient use of the network will be crucial for them to keep costs down. This is where network slicing comes in.

Network slicing is a unique technology in 5G, where the network can be logically sliced end-to-end to deliver customizable service performance. Service providers will be able to slice the network into segments and offer different segments of the network to different enterprise customers and make sure they receive the performance level they're paying for.

Of course, slicing comes with its own challenges, including its technical complexity and the fact that it's cross domain. Consequently, we've been working on several advanced AI techniques to help customers prepare for the challenge of operating such complex systems. The future of networks ultimately lies in cognitive systems, where networks can apply a combination of machine learning and machine reasoning, which is built from knowledge base and reasoning engines to generate conclusions.

This technique is not only relevant to network slicing, but any complex network operations, because we often don't have enough data, or enough labels to train all the possible scenarios. This approach, however, enables the machine to learn on its own and make critical decisions itself without previously being trained or told to do so.

We believe this entire approach, along with intent-based operations, will be a critical step in making 5G operations as autonomous as possible. Exciting times are just around the corner.

This is how network operations can make 5G systems resilient.

Read all about driving 5G monetization through intent-based network operations.

Read more about Managed Services.

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What is the relationship between AI and 5G? - Ericsson