Artificial Intelligence and Machine Learning for Automated 5G Network Monitoring
All networks, whether transport, telecommunications, IT hubs and even entire cities, need proper monitoring and management. Sophisticated processes (and the people who understand them) are required for the kind of deep network drill-down that reveals the causes of problems and ways in which they could be prevented. Similar processes are essential to handle any issues that do slip through the net, to analyze their effects, and to implement damage limitation and permanent solutions.
Byte-level analysis of high-quality data makes these processes possible. For telecommunications network operators, such data are today generated in massive volumes. With the number of devices connected and communicating within the Internet of Things, and the high-speed, high-bandwidth possibilities of 5G, traditional data collection and analysis is no longer sufficient. That’s where artificial intelligence and machine learning play a vital role.
These technologies won’t yet replace old-fashioned human know-how. For example, when a wholly singular issue arises in a telecommunications network, it can often be specialists’ own experience, skills and contextual knowledge that lead to a solution. But there are limitations to this, and some repetitive aspects of network management are better left for AI to handle; this reduces the risk of human error, and frees up the experts to focus on tactical, strategic and business decision-making.
What role does AI play in automated 5G network monitoring for telecommunications?
AI enhances traditionally-defined baselines by allowing automatic calculations, using many different prediction models, to establish the probability that a given parameter will be reached or exceeded. The process becomes even more robust when ML is deployed in network management for anomaly detection. Once detected, AI can search for the original cause of an anomaly and find the answer quickly and efficiently. The network can group similar events together, even if a single root cause is responsible for many different symptoms. From the business perspective, this means automated monitoring for customer-focused issues such as KPIs and SLAs, using data direct from the system.
We’ve already touched upon the importance of data for optimized network management.
It’s worth mentioning here that well-organized, high-quality historical network data will allow a telco to implement AI/ML for 5G network management in a very short time – even as little as three months. So, even if you are planning network reorganization for 5G and IoT at some time in the future, it’s worth getting your data organized now.
With the data in place, products such as Comarch Artificial Intelligence (AI) Control Desk simplify and optimize the network management process for telecommunications operators. Such products will be essential sooner rather than later, as we’re rapidly moving towards a point when networks (and the data they receive and generate) will be so complex that monitoring and maintenance will only be possible using automated systems.