Digital Twin Networks (DTN) for 6G Autonomous Wireless Slicing: Real-Time Channel Emulation, Open RAN Orchestration, and Zero-Touch SLA Guarantees

Abstract

Abstract—The realization of Sixth-Generation (6G) wireless communications envisions hyper-dense, disaggregated, and heterogeneous physical radio environments supporting concurrent ultra-reliable low-latency communication (URLLC), extreme mobile broadband (eMBB), and massive machine-type communication (mMTC). To satisfy stringent multi-tenant Service Level Agreements (SLAs) without manual intervention, 6G architectures require Zero-Touch Network and Service Management (ZSM) driven by Open Radio Access Network (O-RAN) principles. However, executing live reinforcement learning policy exploration or dynamic radio resource slicing directly on physical radio equipment introduces catastrophic risks: exploratory radio frequency (RF) misallocations induce severe packet loss, drop critical vehicular telemetry, and violate regulatory spectrum spectral efficiency thresholds. Digital Twin Networks (DTN) establish an essential architectural paradigm: creating a real-time, high-fidelity virtual replica of physical radio propagation, user mobility dynamics, and O-RAN disaggregated protocol stacks. Operating as an isolated sandbox, a DTN enables Near-Real-Time and Non-Real-Time RAN Intelligent Controllers (Near-RT / Non-RT RICs) to test counterfactual resource allocation policies, train deep reinforcement learning xApps/rApps, and predict network anomalies before executing closed-loop actuation on the physical network. This paper delivers an exhaustive, mathematically formal technical investigation and empirical benchmark of Digital Twin Networks for 6G autonomous network slicing. We formalize physical-virtual state synchronization divergence under continuous stochastic telemetry streams, establish real-time neural Ray-Tracing surrogate models for millimetric (mmWave) and sub-terahertz (sub-THz) physical channels, and model multi-agent deep reinforcement learning (MADRL) slicing optimization. Furthermore, we construct an end-to-end O-RAN software-defined radio (SDR) testbed evaluating 100 virtualized slices across autonomous driving, industrial robotics, and holographic streaming. Our empirical findings demonstrate that DTN-guided xApp orchestration achieves a 99.4% SLA conformance rate, slashes physical slice handover latency by 68.2%, and eliminates live exploration dropouts while bounding virtual-to-physical state synchronization drift beneath 4.8 milliseconds. Finally, we formulate open challenges in ultra-scale graph DTN scaling, federated multi-carrier digital twins, and zero-trust cybersecurity interfaces.