Navigating AI/ML in Telecommunications: A Director of Network Operations Guide
Artificial intelligence and machine learning (AI/ML) have brought about a paradigm shift across various sectors, and telecommunications is no exception. As a Director of Network Operations, harnessing the power of these technologies can be instrumental in improving your team’s efficiency, reducing downtime, and enhancing the overall user experience.
However, navigating the landscape of AI/ML can be complex, especially given the unique characteristics and challenges of the telecom industry. This guide will explore key considerations and strategies to apply AI/ML in telecommunications effectively.
The Importance of Real-Time/Fast-Time Data Monitoring
In network operations, where every second counts when an issue arises, the timeliness of data monitoring is vital. Traditional batch or bucketed Key Performance Indicator (KPI) data often falls short of providing the immediate insight needed to prevent potential service disruptions. AI/ML, when applied to real-time/fast-time data(see my previous blog on the difference — HERE), allows for prompt anomaly detection, ensuring high-quality service delivery and robust network performance.
Choosing the Right Machine Learning Technique
While machine learning techniques are vast and diverse, not all are equally suited to address the nuances of telecom data. Traditional methods like logistic regression and support vector machines (SVM) may lack the flexibility to adapt to rapid network changes. More advanced techniques like reinforcement learning and deep learning offer sophistication, but their complexity and applicability can be challenging when dealing with typical telecom data. Therefore, it’s crucial to select a machine-learning technique that aligns with the specific requirements of your network operations.
Harnessing the Power of the EWMA Model
When it comes to telecom anomaly detection, the Exponential Weighted Moving Average (EWMA) model holds a unique place due to its agility and effectiveness. Unlike many other statistical techniques, EWMA prioritizes recent data points, becoming a swift and efficient tool for anomaly detection. But at Anritsu, we’ve taken this methodology to new heights.
Our advanced application of the EWMA model goes beyond just examining a single dimension. Instead, it intelligently analyses multiple dimensions in the telecom data like network nodes, devices, calling and called numbers, and subscribers. By incorporating all these variables, the model provides a comprehensive and deep understanding of the telecom ecosystem, ensuring no anomaly is left unnoticed.
In addition, our enhanced EWMA model gives substantial weightage to the count of the subscribers affected by a potential anomaly. By tracking and analyzing the number of affected subscribers, our model prioritizes significant network issues that could impact the user experience on a broader scale. Moreover, it provides a list of affected subscribers, giving operations teams the information they need to address issues promptly and efficiently.
We’ve implemented seasonality adjustments based on weekly data points to better reflect recurring patterns in network operations. This innovative enhancement captures trends and rhythms in the network performance data that could escape attention, increasing the overall accuracy of our anomaly detection efforts.
Another key strength of Anritsu’s enhanced EWMA model is its built-in root cause analysis capability. Upon identifying an anomaly, the system generates insights into what is likely causing the issue. This critical information helps operations teams zero in on the root cause faster, leading to quicker resolution times and a more stable network environment.
By amalgamating all these facets — multidimensional data analysis, subscriber impact monitoring, seasonality, and root cause analysis — into our enhanced EWMA model, we’ve created a robust and highly efficient tool for real-time anomaly detection in network operations. This tailored approach helps ensure a superior network experience for subscribers and a manageable workflow for operations teams.
AI on Top of the ML
So if you’ve followed along this far, another valuable layer is worth mentioning, and this makes network operations more efficient — using
artificial intelligence techniques on top of the EWMA to apply a custom AI-applied volume control. This feature allows us to fine-tune the number of cases/issues identified based on the specific needs of the operations team. In practical terms, we can modulate the flow of generated alerts or cases to match operational teams’ capacity and priority settings. Instead of overwhelming our teams with a barrage of alerts, we provide a manageable, prioritized list of potential issues, each tailored to the team’s capacity and the situation’s urgency.
In essence, the enhanced EWMA model at Anritsu, with learnings over the last eight years empowered by AI, forms a powerful tool in our anomaly detection arsenal, finely tailored to the needs and realities of modern network operations.
The Most Efficient Monitoring
One of the significant advantages of our enhanced EWMA model at Anritsu is its remarkable efficiency and scalability, which is critical in the age of Bigger Data and cloud computing. The platform-agnostic model can be deployed seamlessly across any cloud environment, providing significant flexibility to network operations. Moreover, its robust design enables it to handle large-scale telecom data effectively, even with networks with over a hundred million subscribers. Regardless of the size or complexity of the customer base, the EWMA model parses through every bit of data to ensure accurate and timely anomaly detection. Its ability to swiftly process massive datasets and identify potential disruptions without compromising performance makes it an exceptional tool for maintaining network reliability and enhancing user experience.
In conclusion, while the journey to effectively apply AI/ML in telecommunications, particularly in Network Operations, is complex, it is also full of opportunities for innovation and improvement. So for a Director of Network Operations, with careful consideration and strategic implementation, these technologies can significantly enhance how to manage and optimize telecom networks and promise to be a vital cog in the wheel of automation.
