Generative Reality Digital Twin (GRDT)
AI is running your network. Who proves it can be trusted?
The Gap
AI makes decisions in milliseconds, and even a small change can have ripple effects across a live network. Every AI decision starts with an intent. The first step is turning that intent into the right action, without ambiguity. The next is understanding how that action will affect the network before it is deployed.
Trust therefore requires validating every proposed action against the right data and realistic conditions before it reaches the live network.
You need AI to test AI. VIAVI Generative Reality Digital Twin™ (GRDT) is built to do exactly that.
What is GRDT?
GRDT is a synchronized digital replica of your network, spanning one or more federated domains and trained on high-fidelity, live, real-time network data. It generates conditions that have not happened yet, allowing you to see how the network would respond before they do.
By validating AI decisions before deployment, GRDT helps accelerate the journey to trusted network autonomy, reducing operational complexity and giving teams the confidence to focus less on managing incidents and more on delivering new services, better customer experiences, and business growth.
Trust AI and take the next step towards full network autonomy.
Why GRDT?
Only GRDT integrates these three essential functions to deliver a digital twin with unprecedented levels of accuracy and scope.
Where does it apply?
Where there is no tolerance for failure:
Disambiguate AI intent: Every AI decision starts with an intent. The first step is turning that intent into a clear, unambiguous action.
See the ripple effects before you make the change. Test how a change in one part of the network plays out everywhere else, before it goes live.
Find the real cause, fast. Trace a problem to its source across the whole network, from radio to core.
Catch weak points before they become outages. Surface risks that only appear under real conditions, and prove the fix before you deploy it.
Rehearse the scenario before it happens. Emergency incidents, coverage failures, sudden congestion, new classes of AI traffic.
Get ahead of 6G and quantum-safe security. Validate against traffic and encryption overhead that no live network carries yet.
Why VIAVI?
Start with One Domain
GRDT is the destination. Every solution below shares its foundation and is validating networks today, one domain at a time. Turnkey, or integrated with what you already run.
RF Propagation Digital Twin
Industry-first ISAC-optimized raytracing with patented real-time channel emulation.
- Patented compression algorithm for real-time channel emulation
- Calibrated via VIAVI field instruments or VIAVI NITRO Location Intelligence
- Integrated with the VIAVI Vertex channel emulator
Powered by VERTEX, TM500, Ray tracing, Field instrumentation
RAN Simulation Digital Twin
Site-to-network-scale UE emulation with ray tracing, beamforming, and AI-RAN training.
- Site-scale: deep UE emulation with raytracing, beam, coverage, and interference validated at the individual site.
- Network-scale: UE modeling and scenario generation from one site to multiple cities, rApp/SMO validation, AI training datasets grounded in real radio conditions
Use Cases
- mMIMO and beamforming Uplink/downlink traffic performance
- Interference management
- Drone swarm scenarios
- Energy-saving application behavior
- AI-RAN training and Data4AI pipelines
Powered by TM500, Ray tracing, AI RSG, CyberFlood, Field Instrumentation
Intent-based RAN Optimization Blueprint
Continuous optimization loop from operator intent to executed change
Use Cases
- Massive MIMO · Change validation · Energy savings ·Traffic steering · Capacity planning · Progressive cluster dimensioning to L4/L5 autonomy
- On NVIDIA GPUs. Integrates through TM Forum APIs— augments the existing RAN stack, no rip-and-replace.
Powered by AIOps, AI RSG, Location Intelligence + NVIDIA
IP Transport Configuration Blueprint
A closed-loop pipeline from natural-language intent to a signed, replayable verdict, NVIDIANIM-served, scaling next with GNN
Use Cases
- Capacity planning and analysis, configuration changes, traffic engineering validation of new services, new vendor validation
- NVIDIA NIM/Nemotron, cuGraph GNN
Powered by NITRO® AIOps, TestCenter, NVIDIA : NIM/Nemotron, cugraph GNN
Core Emulation Digital Twin
Emulates core traffic and signaling so cApps can be validated against realistic core behavior before deployment.
Use Cases
- cApp validation against emulated core traffic
- AI inference testing at the core
- AI-driven network load balancing
- Production-grade SMO testing at core scale
NTN Validation Testbed
Validates end-to-end connectivity and QoS across LEO, MEO, and GEO networks.
For LEO, MEO, GEO:
- Validates end-to-end connectivity and performance
- Measures Quality of Service over large coverage areas with different types of UEs
- Measures end user application performance while coping with distance, speed and mobility of both satellite and UE
- Assess reliability and stability
Who Tests the AI? VIAVI’s Case for The Realistic Digital Twin
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