This paper surveys how “digital twin networks” could help run future 6G mobile systems. A digital twin network is a high‑fidelity, real‑time virtual copy of the physical radio network. The authors sort existing work into two main architecture types: passive monitoring twins that observe and report, and active control twins that close the loop and send actions back to the real system.

The survey digs into four enabling technologies the twins need. Ray‑tracing is highlighted as a core simulation tool; it follows radio rays through a detailed three‑dimensional model of buildings and materials to predict signal behavior. Reconfigurable intelligent surfaces (RIS) are treated as active physical devices the twin can control to steer signals. Artificial intelligence (AI) is the bridge that turns twin predictions into control decisions. Mobile edge computing (MEC) puts the heavy computation close to users to cut delay.

The authors go beyond descriptions. They extract and compare the mathematical and computational complexity of current solutions to test practical feasibility. To make fair comparisons across varied proposals, they introduce a normalized classification that rates designs by latency class, memory needs, hardware dependence, and scalability trends. They also organize how different AI methods are used to optimize edge routing and resource allocation.

This work matters because 6G will need much more proactive and deterministic control than past networks. New services will demand ultra‑reliable low‑latency communications (URLLC), enhanced mobile broadband (eMBB), and support for massive machine‑type communications (mMTC). High‑frequency bands such as millimeter wave and terahertz are attractive for 6G but are very sensitive to physical blockers and changing environments. Digital twins that simulate the environment and act in real time could help meet these strict targets and support use cases like smart cities, Industry 5.0 manufacturing, healthcare, and smart grids.