Network digital twins (DT) are becoming more popular among telcos worldwide as a way to address various business challenges — from sales and marketing, network operations and customer experience, to IT operations, network planning, and more. This piece explores practical applications of network DTs and how they’re empowering businesses to achieve their autonomous operation objectives.
The Power Behind Network DTs
A network digital twin is a large-scale model that mirrors the real physical network across its life cycle and is continuously updated from design, operational data, and sensor data (real network traffic data). It can use simulation, machine learning, and reasoning to help make decisions or check the decisions made by machines or network operators. It can also simulate what-if scenarios to apply the changes efficiently. DTs can improve the operational efficiency and network quality for telcos. Some key examples are:
- Network Change Management: Network digital twins can be used to test new network configuration changes before they are deployed in the real world. This can help to identify and fix problems early in the process, saving time and money.
- Network Performance Improvements: Network digital twins can be used to analyze network performance and predict the performance of various services and networks. It can identify the potential degradations and assist in preventive steps.
- What-If Scenarios: Digital twins can be testbeds for assessing scenarios related to network planning to determine the best configuration. DTs can create what-if analysis of new networks. This can help to identify and fix problems early in the process, saving time and money.
- Validating Machine Decisions in Autonomous Operations: DTs can help in validating the closed-loop automation decisions made by AI/ML models before decisions are implemented in the network. This will help in improving the trustworthiness of autonomous systems.