How to optimize network operations in times of change

As the impact of the coronavirus pandemic has changed many aspects of people's work and life, one area that has changed is the global Internet. As many people work remotely from home during the pandemic, the use of mobile devices has reached an all-time high, data upload and download rates are also increasing, and the number of video conferences has surged.

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As many organizations and individuals have changed their internet consumption habits, this has meant that telecom operators have had to adapt quickly to these changes. This has also led to a greater reliance on network performance as many countries and regions rely on digital infrastructure to keep their economies going.
As these usage and behavior patterns continue to change, many network events that were accurately predicted in past years are now more difficult to predict. However, with these challenges, many telecom operators are facing greater pressure to generate revenue and profitability.
The following article reviews some of the specific challenges that telecom operators face during these transformative times, and then explores how AI and machine learning can be used to monitor and improve network performance.
The Challenge of Managing Network Performance and Usage Patterns During a Time of Change
Before the coronavirus pandemic, broadband consumption patterns around the world were predictable - usage would drop during the day when most people were at work or school. In the evening, global internet usage would increase.
Today, employees' parents and children are at home all day, and usage of everything from social media to video conferencing has skyrocketed. In a world where network downtime cannot be predicted, many network providers struggle to meet user needs. Therefore, they need to minimize network downtime and continuously predict network traffic to adapt to changing usage patterns.
Uplink traffic changes causing network issues
Before the outbreak, most people's Internet applications mainly used downlinks, including opening web pages, downloading files, and streaming videos.
Now, with more people working from home, many are uploading data at significantly higher rates. Whether it’s for distance learning, video conferencing, or uploading to social media, networks are not designed to handle so much uplink traffic.
This means that while telecom operators need to monitor faults in their networks, they also need to be able to predict future needs and increase support capabilities.
Likewise, now that many telecom operators’ employees need to work from home, they also need to manage the network remotely. Since their existing tools are not built for this, telecom operators need to upgrade their tools so that their employees can better manage the network.
In short, they need to use resources more efficiently and take advantage of autonomous network monitoring. This is where artificial intelligence and machine learning come into play.
Application of artificial intelligence and machine learning in network monitoring
Many telecom operators are now using big data, machine learning, and artificial intelligence to monitor and optimize their networks.
Although we are in the early stages of adopting AI in the telecommunications industry, the fact that communications networks are so complex and the volume of data is so huge makes the possibility of preventing disruptions increasingly important.
To take advantage of these emerging technologies, two main applications of AI and ML in communications networks should be considered, including:
Anomaly Detection: As mentioned above, because consumer behavior and data usage patterns have changed dramatically, it is impossible to detect anomalies with static thresholds. Instead, unsupervised machine learning techniques can be used to learn the normal behavior of individual indicators alone. As this normal behavior continues to change, the anomaly threshold will also change automatically, resulting in increased granularity and fewer false positives.
Demand forecasting: With increasing reliance on network performance, accurate demand forecasting becomes more important than ever. Similar to anomaly detection, AI and machine learning can ingest 100% of the data to predict user demand, so appropriate network settings can be provided in a timely manner.
Now that we have discussed the applications of AI in communications networks, let’s review actual use cases to understand how telecom operators are leveraging data.
Use Case : Artificial Intelligence for Fixed Broadband Access Networks Many telecom operators have transformed their role into a fixed and mobile service provider, which may include multiple complex technologies such as:

• Fiber to the home, node or curb
• Digital Subscriber Loop (DSL)
• Hybrid Fiber Coaxial (HFC)
• WiFi

Satellite broadband Each of these technologies will experience user uplink and downlink events such as throughput drops, packet loss and many other key performance indicators, which means each technology needs to be monitored in real time.
Through a correlation engine that accompanies each anomaly, AI-based monitoring solutions are able to link the incident to the following events.

• Uplink throughput decreases
• A surge in upstream code errors (CERs)
• The upstream signal-to-noise ratio (SNR) decreases

Based on these events, telecom operators can detect anomalies faster and are able to notify customers in the area of ​​service degradation. And, by accurately identifying the associated anomalies that caused the event, their technical teams are able to resolve the problem faster than before.
Summary of the adoption of AI technologies in the telecommunications industry:
The global economy is becoming increasingly complex and more dependent on network performance than ever before. From shifting consumption patterns to surges in uplink traffic, telecom operators have had to adjust their networks quickly and, in many cases, remotely.
For these reasons, many telecommunications companies are beginning to adopt artificial intelligence and machine learning for network monitoring. In particular, two major applications of artificial intelligence in communication networks are anomaly detection and demand forecasting.
While it’s still early days for AI adoption, global changes are making these emerging technologies more important than ever, improving performance, increasing efficiency, and providing businesses with opportunities to stay ahead of the competition.