Mobile network network controller

Overview
Ericsson Transport Automation Controller is an intelligent, cloud-native transport controller that uses AI and machine learning to provide advanced analytics and automation for microwave, IP and optical fronthaul networks. Developed to complement and add value to the existing Transport hardware portfolio, this software product transforms data into actionable insights to enable service providers to understand behaviours, recognize patterns, see trends, and detect anomalies in their transport network so that they can quickly and efficiently take a proactive approach in managing their transport network.

Key capabilities

• AI and machine learning driven analytics and automation for transport networks
• Near real-time root cause analysis that reduces troubleshooting time and manual work
• Continuous visibility across microwave, IP and optical fronthaul domains enabling predictive and proactive operation
• Vendor-agnostic, standards-based, multi-domain solution
• Cloud-native architecture with an intuitive user interface, map-based visualizations and configurable dashboards

Benefits

• Save time and cost: near real-time root cause analysis reduces troubleshooting time, minimizes manual tasks, reduces work orders and unnecessary site visits.
• Enhance performance and user experience: move from reactive troubleshooting to preventive actions to improve network quality and reduce churn.
• Leverage AI/ML insights: automated analytics convert operational data into actionable insights to optimize capacity and network performance while lowering costs using existing network resources.

Product highlights
As an automation and analytics platform, Ericsson Transport Automation Controller enables service providers to gain continuous, service-centric visibility across the entire transport domain and supports closed-loop automation. The product is designed to allow operators to act proactively and stay ahead of potential challenges affecting 5G transport network performance. It complements existing transport hardware by transforming operational data into patterns, trends and anomalies that drive efficient decision-making and automation.

Features / technical specifications

• Supported domains: microwave, IP and optical fronthaul networks
• Architecture: cloud-native, multi-domain, standards-based and vendor-agnostic
• Analytics: AI and machine learning for advanced analytics, anomaly detection, trend analysis and root cause identification
• Operation model: continuous visibility and near real-time analytics for predictive and proactive operations
• User interface: highly intuitive UI with map-based visualization and configurable dashboards
• Primary use cases: reduce network complexity, boost operational efficiency, optimize capacity and network performance, lower costs with existing resources, improve end-user experience