AI-DRIVEN ASSURANCE

Build an AI-Driven Intelligent Network

While 5G core capabilities and advanced use cases are compelling, deploying this innovative technological shift presents inherent challenges. To successfully launch and manage a 5G cloud-native network, operators cannot rely on past manual processes and traditional monitoring tools.

The Critical Role of 5G Automated Assurance

In the past, assurance activities and policies have been primarily manual, time-consuming, and reactive. Automating and orchestrating assurance workflows can help realize 5G innovative capabilities, laying the proper foundation for operating 5G cloud-native networks.


Automated assurance powered by AI and Machine Learning (ML) enables quick and effective network and service performance validation by analyzing immense amounts of data generated by the network, which no human can do. In addition, it can solve complex pattern recognition problems and identify patterns over time and across different data sources, receiving alerts if a specific KPI breach occurs.

Key Benefits

  • Rapidly identify performance issues across all services.
  • Analyze immense amounts of data generated by the network, which no human can do.
  • Solve complex pattern recognition problems and identifies patterns over time and across different data sources.
  • Receive alerts if a certain KPI breach occurs.
  • Optimize the network in the most efficient way without the need for manual adaptation, accumulating experience over time.

“Automatically detecting network anomalies is top AI/ML use case to improve service quality.”

(RADCOM 5G Assurance Survey, May 2021) 

With advanced AI and ML algorithms, the RADCOM ACE solution enables networks to operate in a way that far outweighs networks of the past, by:

Optimizing network performance: use advanced AI tasks such as anomaly detection to allow engineers to remain focused on handling only critical customer-affecting issues.

Smartly planning network capacity: create a long-term forecast for various performance and quality indicators and use it to plan future network capacity smartly.

Improving the customer experience: utilize AI/ML to gain insights into network encrypted traffic, allowing you to understand the customer’s Quality of Experience (QoE).

 

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