Three ways digital twins can make 6G better

By Sulagna Saha January 28, 2026

Collected at: https://www.rcrwireless.com/20260128/test-measurement/three-ways-digital-twins-can-make-6g-better

Experts believe that digital twins have the potential to revolutionize mobile networks, and will play a critical role in amping 6G’s performance  

A digital twin is a virtual rendering of a physical or digital object, so accurate that it reflects the behaviors, peculiarities, and nuances of the object down to the last detail. If the real-world object has any grand features, it will have them; if it has defects, it will have them too.

“Digital twins aren’t just animations; they’re high-fidelity simulations,” explained Siemens’ CTO and CSO, Peter Koerte, in an interview with Wired. “Powered by AI, they don’t just simulate possibilities—they analyze countless scenarios, identifying the most likely outcomes and optimal solutions.”

At a high level, a digital twin is made up of five core components — a mathematical model or the virtual representation of the real-world object or system; data sources that it pulls data from; a feedback system that creates a bi-directional connection between the real object and the model enabling continuous tuning and pruning; an analytics engine — often powered by AI/ML — to make sense of the input data, simulate future scenarios, and run predictive analysis; and lastly, an UI to publish the output for teams to see.

There are broadly two kinds of digital twins that we see today — one that replicates physical products or components, and is used in product development; second, that replicates systems and processes, and is used in behavior prediction and lifecycle monitoring. Both kinds rely on real data to accurately mimic and optimize their real versions.

Uptake across industries

A 2023 market research found that approximately 75% businesses invest in digital twin technologies in some form or fashion. Deployment has risen across industries in the past couple years as research continues to show its potential in anticipating scenarios accurately — and in a fraction of the time of regular prediction tools.

Currently a disruptive force powering Industry 4.0, the technology is transforming high-end industrial complexes, its use often spanning quality control in Smart Factories to drug manufacturing in pharmaceuticals. More recently, the technology has also found use in groundbreaking initiatives such as developing solar models — a project co-developed by IBM and NASA, and creating digital clones of the human brain — and the human heart. 

Digital twins for 6G

There is a growing consensus in the industry that digital twins can unlock new use cases for 6G networks. 6G represents a significant evolution in wireless connectivity. It has been described as the springboard for advanced technologies, like metaverse, VR/XR, autonomous vehicles, smart cities, and industrial IoT. Use cases for 6G networks range from schools and academic institutes to hospitals, tourism, enterprises, and data centers. But, to take it to the next level where operators can deliver optimum resiliency and always-on connectivity without breaking a sweat, digital twins are needed. 

A network digital twin can mirror the complex 6G ecosystem right from its physical infrastructure layer where network devices live to network operations all the way up to the software plane. It can simulate the dynamic interactions and dependencies, the states and operating conditions — and fill gaps in data with synthetic data creating forward-looking demand and failure scenarios. This is groundbreaking for network management.

“What-if” scenarios: One thing a digital twin is especially good at is generating hypothetical scenarios. Using a combination of historical and real-time data, it can generate various scenarios. Operators can measure network key performance indicators (KPIs) to tally with predefined standards.This allows operators to not only take measurement of the network performance at the moment, but also for a multitude of future scenarios that may happen.

The best thing of all, the scenarios can be orchestrated and studied in a completely risk-free sandbox environment without ever touching the real production network. Additionally, digital twins can surface future performance trends that are key to understanding what to expect and how to avert a crisis.

AI training and inference: 6G is often described as an AI-native network. Explaining what that is in practice is however a tad complex. Blue Planet’s VP, Kailem Anderson, shined light on the idea in an earlier interview with RCR Wireless News. He said,“[AI] will be built into the chipsets, the hardware protocols, the software stack and various abstraction layers so that the network is truly intelligent. What does that mean? It means 6G will truly embrace principles around self-healing, self-optimizing, self-organizing, so that the network operates in a truly declarative or intent-driven way.” 

This is one of the reasons why digital twins are relevant in the context of 6G. AI is all about good data, and the data output from digital twins can prove valuable for training AI/ML algorithms embedded in 6G networks. The context-rich information can not only keep the models updated, but also ensure accurate inferences leading to sound decision-making, a non-negotiable for self-driving networks. 

Power optimization: With each new generation of wireless connectivity, the energy cost tends to go higher. 6G’s extreme speed is also accompanied by heavy energy demands — especially with AI being at the core of its vision. That adds to its environmental impact, introducing complex grid management for many aging infrastructure.

Digital twins can help optimize the power draw of 6G networks. By providing real-time insights on traffic patterns and energy usage during peak- and low-demand periods, it can enable operators to optimize utilization. It can also help them to predetermine demand variations and work out the most energy-efficient configurations ahead of time, to ultimately reduce energy usage and the resulting carbon footprint. With AI-powered digital twins, operators can also dynamically turn on and off network resources, based on the hour of the day and traffic load, for minimum wastage.