AI Driven Tool in 5G network

Along with 5G, network operators are embracing AI-driven techniques in order to make the production network smart enough to track usage patterns and predict anomalies. 5G network needs to be capable of self-optimization with AI and network analysis tools. This insight will help operators segregate their customer base into micro-segments and cater services as per the segments’ preference. Data insights obtained from a live network can power an AI-driven tool for predictive root cause analysis. Customers can be assured of uninterrupted Quality of Service (QoS) with AI-driven network analysis and anomaly detection mechanism.
Managing multiple Radio Access Technology and densified allocation of small cells along with virtually deployed core network functions or components in an NFV architecture would be a complicated and a daunting task. An automated tool which can sit in the network and provide descriptive analytics for identifying the exact cause of failure along with visualization of probable failures in the call flows would be ideal. Thus a network engineer can avoid manual parsing of logs and KPIs of different interconnected network components and servers. This tool needs to be trained for each deployment so that it can identify anomaly by correlating data and threshold values specifc to that configuration. Hence it is important that operators start training their DevOps tools in the pre-production environment against simulated call flows for different 5G use cases/scenarios. The knowledge DB generated can then be used to help diagnose a network failure in real-time.