The explosion of interest in AI in 2023 has been primarily driven by the widespread availability of Generative AI, but Network Artificial Intelligence (AI) and Machine Learning (ML) have been at work for far longer. In this article, we're highlighting three interesting use cases to build a clearer picture of what’s happening now and where we’re going.
1. AI Enhancing Network End-User Experience
AI is being utilized to help NetOps teams manage the network end-user experience. This involves using AI and ML for efficient data collection, processing, and selection to rapidly identify and expose the most relevant information. AIOps allows network operators to correlate events across the tool stack and other data sources within your network, identify root causes and recurring issues across different environments, and assign collaboration projects for the appropriate operators and teams.
2. AI Training Telecommunication Networks
One fascinating application of AI on the network is in the training of telecommunication networks. The RAN Intelligent Controller (RIC), a cloud-native and a central component of an open and virtualized RAN network, enables the optimization of RAN resources through near real-time analytic processing and providing adaptation recommendations. The RIC allows operators to make the most of network resources, eases network congestion, and supports network slicing, mobile broadband, and mission-critical communications.
But before the RIC can make smart optimization decisions in the future about what actions to take, it needs to be trained with actual data first. This is where a RIC tester can train the network applications by creating a stream of RAN data to test the output decisions of the RIC. This technique ensures RIC developers can test their RIC effectively, promoting the efficient and effective rollout of open RAN networks and architectures, ensuring it is fully trained before it is deployed in live networks.
3. AI Providing Service Assurance
AI can provide service assurance, a vital aspect of network operations. Through AI, service assurance solutions can provide real-time monitoring, helping to identify the root causes of customer experience impacting issues, enabling proactive problem resolution.
AIOps Service Assurance, for example, provides a holistic view of the entire network - end-to-end, irrespective of the vendor. This view is used to automate, optimize, and visualize digital experiences as well as service and network quality across hybrid telco and IT networks. In essence, AI's role in providing service assurance is about governing the digital experience as well as service and network quality with intelligence that monitors, detects, and heals by leveraging local orchestrators.
AI's impact on the network is substantial and multifaceted. From training telecommunication networks, managing network end-user experience, to providing network service assurance, AI is driving a revolution in how networks operate, promising more efficient, reliable, and user-friendly networks for the future. The next iteration of network assurance, intelligence, and testing revolves around digital twins, which include AI as a centerpiece. To remain competitive, network operators will need to build prebuilt functions and vertical market templates to proactively manage the challenges of their live networks. As we continue to develop and refine these technologies, the "AI on the network" will cease to be a mere “buzz phrase” and become an integral part of our network landscape.
The VIAVI Observer solution provides multi-faceted visibility into on-premises, AWS, and hybrid IT environments. It can ingest both packets and AWS VPC Flow Logs to improve visibility and facilitate efficient troubleshooting, enabling NetOps, SecOps, and AWS architecture teams to better understand and enhance the end-user experience.
For more information, see our solutions.