Machine Learning and Artificial Intelligence are playing key roles in NOC operations!
Intelligent network automation using machine learning
A growing number of new opportunities for artificial intelligence (AI) are being created as a result of advances in computing power, cloud architectures, digitalization, and big data analytics. Using AI, we are moving from use cases that mimic human behavior to large, complex systems that leverage human capabilities. In the field of AI, there has been rapid progress in machine intelligence, a discipline that augments the modeling and structuring of machine learning with reasoning and planning techniques.
An intelligent NOC
A NOC is responsible for ensuring network availability and performance efficiency through the management of faults and performance in its role as centralized monitoring and control center for a telecom network.
Technically, the typical NOC challenge involves handling incoming alarms (fault management), identifying the cause of the alarm conditions, and implementing appropriate solutions. Domain experts are required for implementing these solutions. Because technologies and architectures of networks are constantly evolving, it becomes more difficult to implement and maintain them.
The NOC prototype software can manage faults automatically by utilizing machine intelligence techniques. The software includes the following features:
- Using pattern mining techniques, map composite conditions based on historical information (grouping alarms across domains for detection through intelligent grouping).
- Use machine learning to create rules based on the composite conditions
- An approach to detecting incidents based on rules
- It is imperative to document the system or solution to identify root causes and establish appropriate actions.
Artificial intelligence, the topology of the network, and the architecture of the IT infrastructure do not affect the prototype’s rules. Therefore, the component can be re-used and adapted for other applications, including detection and analysis of incidents. Through the constant refinement and development of insights, rules, policies, and workflows, network management will become a largely autonomous process. In addition, we will identify and fix defects before they cause a catastrophe.
Intelligent digital assistants
As a way of utilizing machine intelligence on radio base station sites, intelligent digital assistants can be used. However, installation, configuration, and maintenance are time-consuming and expensive. Technicians can manage issues and diagnose problems with the application. In the end, this reduces the need for on-site assistance while improving quality assurance.
A prototype uses visual object detection technology combined with semantic annotation of product documentation to guide a technician through the task at hand.
In the case of troubleshooting a faulty cable adapter, visual object detection and an augmented reality application can identify and indicate the faulty port, while steps to resolve the problem are also displayed.
As another example, the application can be used to identify and locate various components of a radio base station. A technician can access the documentation for a component by tapping on the image on the screen.
In the process of troubleshooting, machine intelligence is used to prepare the telco knowledge graph. Data input is required from two sources: an image set for the object detector, and a document for the product. Referrals in the documentation to hardware components are linked to the detected objects and vice versa.
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Based on convolutional neural networks (CNN) architecture, the visual object detector detects objects visually. A visual object detector performs the following in a single step:
- Input image pixels are used to extract features
- Analyzing visual objects to predict their type
- A prediction of where the visual object will appear in the scene
- A technician can use the client-server object detector (running on a powerful Graphics Processing Unit machine) or use it as a stand-alone application (running on their smartphone or tablet).
The product documentation is typically provided in HTML or PDF format and follows loosely defined structural guidelines.
Knowledge extraction is the process that converts the content of these documents into a form that can be understood by the application and subsequently presented to technicians. Graphs are created with software using the extracted information.
Network Operations will be greatly impacted by artificial intelligence
Mobile network maintenance and operations will increasingly be impacted by machine intelligence, which is a technology that has been in the lab. Systems can be scaled and made complex, while improving productivity, using network engineering and technology expertise.
Freelancers working for NOCs
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