Digital Twin

A digital twin is a living virtual model of a real network — or a piece of one — that stays synchronised with its physical counterpart through live data, not a static simulation you run once. The value is that you can ask "what if" questions against the twin instead of the production network: test a configuration change, predict how a cell behaves under a new traffic pattern, or rehearse a failure, all without touching live service.

The synchronisation is what distinguishes it from ordinary modelling. Because the twin is continuously fed real telemetry, it reflects the current state of the network, so its predictions stay grounded. Operators use them for capacity planning, change-impact analysis before risky rollouts, and increasingly for failure prediction — spotting that a trend is heading toward an outage while there's still time to act. Fidelity is the perpetual trade-off: a more detailed twin predicts better but costs far more to build and keep in sync, so scope is chosen carefully around the questions you actually need answered.