How Telecoms Can Leverage AI and Machine Learning to Improve Network Performance Management

The first cellular network in the world was launched in 1979 by Nippon Telegraph and Telephone (NTT) in Japan.

Since then, we’ve witnessed:

The first 2G network being launched in Finland, enabling texting and internet browsing (1991).
The ability to stream video, conduct video calls, and more with the debut of 3G (1998).
4G LTE launches in Norway (2010).

And today, the world is quickly becoming 5G-enabled. 5G is the gateway to powerful connectivity to IoT devices, unlocking performance sensitive, low latency enterprise applications.

In other words, telecommunications companies are no strangers to innovation and disruption. In fact, one might say that they are masterfully adept at leveraging technological innovation to deliver new technologies to global markets.

The latest innovation to disrupt the sector is new artificial intelligence (AI) and machine learning (ML) applications that give engineers the power to automate manual task work in network testing and data analysis processes.

This article provides telecom companies with a comprehensive guide to the various applications (and benefits) of implementing AI and ML technologies as well as real-world examples of companies that are winning with these tools.

AI and ML Applications in Network Testing and Troubleshooting

Dave just got home from work. It was a long day and he’s exhausted. He spent most of his day troubleshooting issues with signal strength on a radio access network (RAN) managed by his employer. He spent his day reviewing log files, analyzing network traffic, and performing a host of manual tests (everything from drive tests to spectrum analyses). The result? Inconclusive. So Dave is going to be heading back in the morning to continue his investigation.

Sound familiar? That’s because network engineers have spent decades using manual processes like this for network testing and troubleshooting.

But, that’s all about to change. New AI and ML-powered technologies are going to eliminate manual strain and give network engineers the power to increase both their testing velocity and efficiency.

Here are a few of the AI and ML applications that are revolutionizing the network testing and troubleshooting sector.

1. Network Monitoring

In the past, network monitoring looked something like this:

A network engineer mines their data lake where data from multiple-technologies and multiple vendors across the vertical integration stack needs to be correlated to produce a complete view of network performance.
They analyze traffic to identify data trends, like signs of congestion, packet loss, latency, or other issues that could be affecting network performance.
Data packets are captured and analysis on packet headers, payload content, and network protocols occurs.
A latency analysis is performed by testing the delay in packet transfer between two devices.
Troubleshooting any issues that are identified.

What if we could wave a magic wand and turn this into a single-step process? With AI and ML applications, the first four steps can be taken care of autonomously, giving network engineers more time to focus on troubleshooting complex issues and formulating network strategies.

2. Fault Detection

Since AI and ML models can continuously monitor network data and compare against performance metrics in real-time—this is a possibility. When major deviations from normal operating patterns occur, these AI and ML applications can immediately flag these faults, enabling engineers to respond to network issues faster than ever before.

3. Automated Remediation

One of the greatest benefits born out of network-based AI and ML models is pre-trained ML models that are able to speed up the problem identification process by leveraging data from historical cases that share similar patterns. Based on this historical data, these models are able to present (or even implement) solutions, mitigating a lot of the tedious investigation and diagnosis work.

For example, if an AI or ML model detects a performance issue in your network, it would be capable of triggering a configuration change (like a change in bandwidth allocation) that could resolve the issue almost immediately and without any manual intervention.

Enhancing Data Analysis with AI and ML Models

If you thought that the benefits of AI and ML models were exhausted at the testing and monitoring phase of network performance management, then we have good news: there’s more.

Here’s how industry leaders are using AI and ML tools to perform faster and richer data analysis.

1. Root Cause Analysis

Root cause analysis (RCA) is a line item that can be found in every network engineer’s job description. It’s the process of identifying the source of network issues (ex. congestion, failure of a network element, infrastructure resources, or provisioning issues) so that they can be addressed and resolved.

Applying AI and ML models to root cause analysis can save significant time and reduce the risk of human error. For example, an ML algorithm might analyze historical network data to identify patterns that are associated with specific types of network issues. When a similar pattern is observed in real-time network data, the algorithm can automatically trigger an alert and suggest potential root causes.

Supervised and unsupervised ML models can even be combined to address rare and unprecedented network problems. For example, a company might be experiencing intermittent network outages that engineers have not encountered previously.

To address this, the engineers could use supervised learning algorithms to analyze historical network data and identify common root causes of network outages. They could also use unsupervised learning algorithms to analyze real-time network data and detect any unusual patterns or anomalies that might be causing the issue.

2. Network Optimization

AI and ML algorithms create a world where telecom networks can be self-optimizing. These models can assess network quality based on service KPIs and real-time data to implement interventions aimed at improving overall network performance.

Some of the possible AI/ML interventions are as follows:

Network audits based on preconfigured performance KPIs
Automated benchmarking based on real-time data
Auto-tuning configurations that are triggered when network performance falls below a pre-defined threshold.
Automatic antenna configurations to remediate coverage issues

3. Predictive Analytics

Once you unlock the sea of data that these tools make available to you, it would be foolish not to leverage it proactively.

Here’s how a Senior Analyst at Everest Group explains the role of AI in predictive analytics:

"The technology enables networks to learn from past instances using massive amounts of data through predictive analytics. It collects network telemetry data, recognizes trends, and forecasts network difficulties that might negatively impact user experience and offers potential solutions to the issue." — Titus M, Senior Analyst, Everest Group (source: DataCenter Knowledge)

These models can also assist with capacity planning and fault prediction. By analyzing traffic patterns and performance trends, engineers can determine where their time is being either over or under-utilized. These AI/ML algorithms can also predict when a network component is likely to fail, enabling engineers to replace or repair network hardware before it becomes a larger issue.

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Real-World Success Stories in Network Troubleshooting and Analysis

Though these AI and ML models might feel like technologies of the future, the reality is that they are technologies of the present. In fact, a number of telecom companies have already implemented AI and ML technologies similar to the ones we’ve introduced.

Companies like AT&T are leading the charge:

“We are implementing AI to help us to identify where these breakpoints are, and help to repair those in an automated way without human intervention. This goes for hardware failure, software failures.” — Mazin Gilbert, VP of Advanced Technology at AT&T Labs (Source: MindTitan)

Here is an example of a B-YOND user that was overlooking network testing, leading to a reduction in innovation and reduced speed-to-market. By implementing AI/ML troubleshooting models, they were able to increase their test capacity by 10x and reduce the costs associated with testing by 50%.

Network Testing Continuous Validation Case Study

And here is an example of a B-YOND customer that was struggling to keep operating expenses down in the face of increased network complexity. By using AI/ML models for testing and predictive analytics, the company was able to achieve cost savings of $9M ($3M in pre-production and $6M in production) per year on a suite of 25,000 test units.

Machine Learning Infused Development Operations

ML-Infused DevOps Case Study

Future-Proofing Telecom Networks with AI and ML Technologies

Many telco companies are facing a somewhat harsh reality: revenue is flattening.

Markets are becoming more and more saturated, creating a price war that feels like a race to the bottom. OTT providers are becoming increasingly competitive with internet-based communication services. And, in many countries, regulatory bodies are squeezing revenue even more with policies aimed at protecting consumers.

With such intense market pressures, most telcos have no choice but to look internally for opportunities to tighten up their operational expenses (OPEX), improve customer satisfaction, and, in turn, increase profitability.

One of the best tactics for telcos looking to future-proof themselves is to improve network capabilities to increase consumer appeal. For example, Ericsson’s 2022 Mobility Report projects that average data consumption per smartphone is expected to exceed 19 GB per month in 2023. Furthermore, they predict that 5G mobile subscriptions will reach 5 billion by 2028 and that video will account for 80% of global network traffic.

Source: Ericsson Mobility Report

By investing in the latest consumer technologies and providing customers with the best network support possible, telecom companies can ensure that they remain competitive. However, these new technologies are also expensive, so most companies will need to offset these infrastructure investments by reducing their input costs.

Downsizing is not a viable or sustainable option with top talent becoming harder and harder to find, so telecoms need to assess their operational processes and identify opportunities to improve efficiency.

Simply put, AI and ML applications present the greatest opportunity in decades for telecoms to enhance their operational efficiency. According to McKinsey, field and service operations currently account for 60 to 70% of most telcos’ operating budgets.

By taking direct aim at this cost bucket, AI and ML applications for network management can provide real cost relief for telecoms.

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Conclusion

Unlike lava lamps, AI and ML technologies aren’t a fad in the telecommunications sector. These solutions are here to stay, and as these models continue to mature, telecoms will be left with a couple of options:

Get on board
Get left behind

But adopting new technologies can also be a risk. How do you identify the best solution? How do you know who you can trust with such sensitive data…?

AGILITY by B-YOND is the leading solution for AI and ML packet capture and data analysis for 4g and 5g networks. And most importantly, B-YOND is a telecom company with an AI and ML solution, not an AI/ML solution that is being sold to telecoms.

If you’re looking to:

Launch new technologies and devices, faster
Handle unique issues
Enhance post-launch optimization and maintenance on new devices
Save time and effort on repeated investigations
Unlock a powerful knowledge base

Then you need to try AGILITY. Setup your free account today and see why we are the leading solution for telecoms looking to automate packet capture and data analysis.

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