Cognitive Analytics Using
Machine Learning

5G service requirements, user behavior and service creation will require closed loop ML networks and applications with self-optimizing and self-healing features.

With this technology, often classified as cognitive analytics, telecommunication companies can develop the two core solutions needed to keep pace with 5G networks:

  • Anomaly Detection

  • Predictive Demand

  • Behavior Recommendation

Anomaly detection


Feature set ensures the underlying network and customer support systems can self-correct and self-organize to keep pace with the high level service requirements (low latency, high reliability) that are required to support mission-critical applications.

Predictive Demand and
Behavior Recommendation


Forecasting user demand ensures sufficient network resources are created, provisioned and scaled in advance. Dynamic scaling of service creation to meet customer consumption is the essential value proposition for 5G service providers.
 
AI/ML-based anomaly detection can be used to detect latency problems for autonomous vehicles, initiating the orchestration layer to spin up new network slices in order to recover performance.
Another example would be to use real-time demand prediction of traffic to spin up and spin down network slices.
 
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