AI-native 6G networks will serve billions of intelligent devices, agents, and machines. Because the industry moves into latest spectrums like FR3 (7–24 GHz), radio physics becomes much more sensitive, shifting the network from a static infrastructure to a dynamic, living system.
This shift demands a fundamental change in how we design, construct, and optimize 6G systems. Traditional “construct and test” methods are not any longer viable. We cannot afford the fee or time to check every AI algorithm in physical environments. To deliver the advantages of AI-native 6G, the industry requires a continuous integration/continuous development (CI/CD) approach to Radio Access Network (RAN) software—powered by physically accurate digital twins.
Powering the 3-Computer solution behind AI-RAN and 6G
The event cycle of 6G for software-defined AI-RAN will closely mirror the AI development cycle, counting on a widely known AI paradigm, the three-computer solution. NVIDIA supports developers across every computer, with hardware and software.
Computer 1: Design and training
This important phase uses the computational power of accelerated computing platforms, akin to NVIDIA DGX and NVIDIA DGX Spark, to speed up your complete design and training workflow. NVIDIA provides a comprehensive suite of specialised software tools to maximise performance:
- NVIDIA Aerial CUDA-Accelerated RAN: A high-performance, real-time, software-defined framework for rapidly simulating and deploying complex RAN systems using GPUs.
- NVIDIA Sionna: A dedicated, GPU-accelerated library for modeling and training the physical-layer of advanced communication systems.
Computer 2: The simulation bridge
The telecommunications industry is moving toward digital deployments, where latest designs are first rigorously evaluated in highly accurate radio frequency (RF) environments. The NVIDIA Aerial Omniverse Digital Twin (AODT) is a bridge for accelerating this transition, moving development from easy simulators to comprehensive, real-time digital twins.
The AODT achieves this through two core abilities:
- Provision of accurate radio environments: It offers a physics-accurate representation of real-world RF conditions, ensuring performance within the digital twin is predictive of actual physical deployment.
- Real-time fabric connectivity: A low-latency data fabric connects complex AI-RAN stacks with the digital RF environments. This real-time link enables true, closed-loop system simulation.
Computer 3: Field deployment
Once a design is rigorously trained and validated within the digital twin, it’s deployed on to the NVIDIA Aerial RAN Computer (ARC)—a high-performance CUDA-accelerated platform for executing RAN functions in the sector. This transition is accelerated by the NVIDIA Aerial framework, which simplifies and automates the traditionally complex strategy of hardening the data-plane algorithms. The framework ensures rapid and reliable deployment of advanced capabilities developed in previous environments onto GPU-accelerated hardware.
Overcoming the three barriers to digital deployment
This brings us to the core mission of AODT:
- To support the simulation of cellular systems with physics-compliant accuracy.
- To drive the virtuous cycle between simulators and digital twins, ensuring that what works within the digital world works in the true world.
To realize fluidity between simulators and digital twins, AODT addresses three fundamental barriers which have historically held back the simulation ecosystem.
Accuracy
Simulation is futile if it doesn’t predict reality. Traditional stochastic channel models often depend on “plane wave” approximations that treat complex antenna arrays as single points. While sufficient for 5G, these approximations crumble under the physics of 6G, which relies on Extremely Large Antenna Arrays (ELAA) and near-field propagation. AODT bridges this gap by delivering a physics-compliant environment that mirrors the true world. Through the use of deterministic, full-wave ray tracing that models individual antenna elements and spherical wavefronts, a design validated within the twin behaves predictably in the sector.
Integration
Advanced RF physics is notoriously complex to implement. While most research teams and vendors maintain their very own system-level simulators in Python, C++, or MATLAB, they rarely have the resources to construct a high-fidelity ray tracer from scratch. AODT solves this by acting as a “physics engine” for your complete cellular ecosystem. By revealing a headless, gRPC-based architecture, AODT abstracts away the complexity of electromagnetics (EM). Keep your existing simulator for network logic, while AODT provides the ground-truth physics within the background.
Operation
Network operators can’t optimize what they’ll’t safely touch. There’s a natural hesitation to deploy aggressive AI algorithms on live networks resulting from the danger of service outages. AODT removes this risk by enabling an operational digital twin that runs in parallel to the live network. This permits operators to modify between real and twinned environments, validating every configuration change or software update in the security of the digital world before it touches an actual user.
A live operational digital twin, as shown in Figure 2, bridges this gap, where network software is tested in a digital twin before deployment.
This permits a production-grade, CUDA-accelerated vRAN software to modify between its live network and the twinned RF environment. This has significant implications:
- Test-before-deploy for zero downtime: Operators can validate critical software updates against a virtual copy of their real-world deployment before pushing it live, for zero network downtime.
- Debug real-world scenarios with repeatability: When a live cell experiences issues, operators can record the true environmental conditions and replay them throughout the digital twin. This permits a controlled, repeatable debugging of the production software.
- Accurate CI/CD pipeline: AODT enables an accurate CI/CD pipeline for network software, where every code change is validated against a practical, large-scale virtual world before being deployed into the live network.
The AODT roadmap
The AODT product roadmap has two crucial focus areas: electromagnetic (EM) propagation, which goals to beat the accuracy barrier, and the platform, which addresses the mixing barrier.
Track 1: Advancing EM accuracy
This track is devoted to advancing the realism and accuracy of the simulated RF environments by constantly refining them to mirror real-world conditions. It enables high-fidelity twins by precisely modeling complex physical obstructions that significantly affect signal quality.
| Product release | Key features and milestones | Strategic advantages |
|---|---|---|
| Foundation (Rel 1.0) |
Core ray tracing with specular reflection, diffraction, and Lambertian diffusion. | Establishes an efficient and accurate baseline for fundamental RF simulation. |
| Mobility enhancements (Rel 1.1–1.3) |
Advanced mobility physics including directional diffusion, custom antennas, O2I/I2O propagation, and dynamic scattering. | Enables realistic 6G mobility scenarios with georeferenced signal tracking for moving objects. |
| Refining realism (Rel 1.4–1.5) |
Environmental integration of vegetation, hilly-terrain DTMs, and calibrated material properties (glass, concrete, brick). | Delivers high-fidelity digital twins by accurately modeling real-world obstructions affecting signal quality. |
| Future (Rel 1.6) |
3D radio-map generation and accelerated EM techniques. | Reduces runtime for large-scale, repeated queries, enabling real-time AI/ML training and big coverage planning. |
Track 2: Constructing the platform
This track focuses on evolving AODT from a single-user application right into a globally accessible, scalable, and integration-ready service. It decouples the simulation engine from individual user workloads, creating a sturdy, queryable service architecture that might be centrally managed, maintained, and constantly updated.
| Product release | Key features and milestones | Strategic advantages |
|---|---|---|
| Research tool (Rel 1.0) |
AODT introduced as a standalone application running on a single GPU with a UI, with latest concepts integrated in C++. | Provides initial power and speed for individual researchers to validate core physics models. |
| Expanding integration (Rel 1.2–1.3) |
Enabled headless operation for servers and introduced a code-based Python API. | Opens AODT to the broader AI/ML and data-science ecosystem, allowing researchers to integrate code and run complex simulations. |
| Pivot to service (Rel 1.4) |
Added inter-process communication (IPC) and a client/server architecture, transitioning AODT right into a dedicated service. | Decouples the simulation engine from the user’s workload and establishes a centrally managed, queryable service architecture. |
| Cloud-native (Rel 1.6) |
Accomplished transition to a cloud-native, headless-first architecture designed to scale across multiple GPUs or data centers. | Establishes AODT as an elastic, accessible data-center-level resource, reducing barriers to entry and enabling enterprise-level use cases. |
Empowering the AI-native 6G era
The transition from 5G to 6G must tackle greater complexity in wireless signal processing, characterised by massive data volumes, extreme heterogeneity, and the core mandate for AI-native networks. Traditional, siloed simulation methods are insufficient for this challenge.
The NVIDIA Aerial Omniverse Digital Twin is a high-accuracy, high-performance, and scalable platform built for this latest era. By democratizing access and unifying the ecosystem around a standard platform, the Aerial Omniverse Digital Twin provides the muse for the AI-native 6G era.
AODT is on the market through the NVIDIA 6G Developer Program. We invite researchers, developers, and operators to integrate this powerful latest service and collaborate with us in constructing the longer term of 6G.
