The Role of AI & ML in 5G Service Assurance

5G is guiding us towards a more wireless world in which we all can stay connected as the services are available 24 x 7 on-demand. Now you can do more on the go, the tasks that used to take hours or minutes could now easily be completed in a few seconds. Nevertheless, this move towards 5G is a seismic transformation of the network infrastructure and management. With this evolution, the Communication Service Providers are also shifting towards automated orchestration of 5G services. They will maintain a distinctive fulfilment and assurance process in addition to various other legacy processes. However, setting up a rigid functionality and interoperability is necessary to work toward complete automation.

Adoption of Advanced Technology

There are many operators who have been adopting artificial intelligence (AI) and machine learning (ML) over the last five years. This is being done to reduce the headache caused by managing repeatable manual tasks and replacing them with applications that can automate them. When we talk about operational support systems, then assurance has always been one of the focus points, where the advantages of AI and ML are observed in real-time performance and contextual analysis for service quality optimization. This in the end helps in predictive fault detection.

We can find various complex return-on-investment models for 5G operations that have leveraged the efficacy of using AI and machine learning in order to manage the costs of 5G. basically, TSPs are intrigued by the positive outcomes these technologies can bring into consideration with various business metrics. For example, there’ll be a lesser number of service issues and network interruptions that leads to a reduction in the churn rate and hence the organization can save on the mean cost to repair.

When you assemble data from multiple sources, you get an accurate and real-time view of the correlation between virtual, physical and logical elements. By doing this, CSPs can get a broader topological view for end-to-end service awareness. Most of the leading vendors have been trying to achieve this in their network inventory for many years.

Defining Dynamic Automation

If we talk about the manual tasks that are related to fault monitoring, performance management, notifications, etc. need highly skilled professionals to analyze the data because the right decision is generally dynamic and contextual. In context to this point of view, logical rules-based decision making lacks the awareness that technologies like AI and ML can bring, particularly in relation to the products and services offered to the customers.

As per research from Omdia conducted on the basis of the investment from CSPs and vendors, about 80% of service providers are experiencing the utilization of AI and analytics and 60% of them are planning to increase their investment in AI tools.

TM Forum Research

TM Forum also conducted research that reported increased use of Artificial Intelligence in the operations industry (AIOps). Though the market segment in this is still small, in the long run, the networks will get fully automated as more than half believe that they will deploy their systems installed with AI in the coming few years.

What’s so special about AI-powered Assurance?

AI is required to perform human-like logical reasoning in instances of service assurance so that there’s more contextual decision making. Here are a few examples:

  • Understanding What’s Normal and What’s Not:

Assurance means analyzing performance data present in the data acquisition layer while designing a ‘normal’ performance deviations view. If at some points transport and access networks are showcasing any type of blockage, then AI and ML can create a dynamic view about which threshold KPIs are anomalous and needs attention and which all are acting as expected and will be back to normal if not touched.

  • Patterns in Alarm Detection

AL and ML are generally used in combination to learn more from network events, alarm patterns and other resolutions that can automatically relate those events against similar scenarios that have occurred in the past to recognize the root causes for the service and network outages. ML can even create a log of best responses for similar events in the future. The automation level will increase with the dynamic self-improvement in the system.

  • Optimization & Remediation

On the basis of pattern analysis, it must be possible for AI to initiate workflows into the network or service operations centre to suggest the resolution for the fault.

Conclusion

With the development so fast, machines are now needed to analyse massive amounts of data generated by the networks on a daily basis. With AI comes various advantages that help in designing next-gen service assurance solutions.

By deploying a cloud-native service assurance solution having built-in AI/ML, the TSPs can use the data that has been collected through systems’ containerised probes.

In conclusion, to make complete utilization of 5G technology, operators are needed to deploy cloud-native assurance solutions with embedded AI. This will offer the most effective way to include AI/ML into the network to help network engineers.

Tags: 5GAiMLTSP
Anubhav Goel

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