Network Digital Twin

Maintains a continuous, near-real-time mirror of the physical network's configuration, operational state, and topology. This is achieved through streaming telemetry protocols like gRPC and NETCONF, which push granular data from physical and virtual network functions to the twin. Unlike periodic polling, this push-based model ensures the digital replica reflects the live network with minimal latency, enabling accurate, moment-in-time analysis.

Goes beyond abstract topology to model the physical layer characteristics and protocol behavior with mathematical precision. This includes:

• Radio propagation models for wireless networks (path loss, fading, interference)
• Queueing theory for packet processing and buffer management
• Full-stack protocol emulation (e.g., 5G NR MAC scheduler, BGP routing logic) This fidelity allows the twin to predict how a configuration change will impact latency, throughput, and signal quality before touching a live device.

The core computational environment that can ingest a proposed configuration change, run it against the synchronized state, and output a quantitative prediction of impact. This engine supports "what-if" analysis by allowing engineers to inject synthetic traffic loads, simulate device failures, or apply new policies. The results are compared against defined intent-based policies to automatically validate whether a change is safe to deploy.

Aggregates data from heterogeneous, multi-vendor infrastructure into a vendor-neutral, canonical data model. It normalizes configuration and telemetry data from diverse sources (e.g., Cisco CLI, Juniper YANG models, O-RAN O1 interfaces) into a single, coherent representation. This abstraction layer is critical for running consistent AI/ML algorithms and automation workflows across the entire network, regardless of the underlying hardware.

Serves as the sandbox and training ground for AI agents that will later run on the live network. Reinforcement learning algorithms can safely explore millions of optimization scenarios (e.g., load balancing, energy saving) in the twin without risking service degradation. The twin also acts as a validation gate for AI-generated configurations, using its simulation engine to certify that an AI-recommended change will not violate a KPI before it is pushed to production via a reconciliation loop.

Exposes all functions—state ingestion, simulation, and validation—through well-defined, versioned APIs. This allows the digital twin to be integrated directly into a GitOps-based CI/CD pipeline for network changes. A proposed configuration commit can automatically trigger a twin simulation, and the results of that validation can gate the automated promotion of the change to the live network, forming a critical safety mechanism in a zero-touch provisioning framework.

The control system architecture that gives a digital twin its operational purpose. It continuously monitors the live network state, compares it against the twin's simulated ideal, and automatically applies corrective configuration changes to maintain the desired state.

• MAPE-K Loop: The foundational reference model (Monitor, Analyze, Plan, Execute, Knowledge) for this process.
• Drift Remediation: The twin identifies configuration drift, and the closed-loop system automatically corrects it.

A management paradigm that provides the high-level business policy input for a digital twin. An administrator declares the desired intent (e.g., 'maximize throughput for IoT slice'), and the twin validates and translates this into specific, low-level configurations before safe deployment.

• Declarative Configuration: The twin models the 'what' of the intent, not the 'how'.
• Continuous Assurance: The twin constantly verifies that the live network is fulfilling the original intent.

The high-fidelity data feed that breathes life into a digital twin. Instead of traditional polling, network devices push a continuous stream of real-time operational state (e.g., interface counters, buffer depths) to the twin using protocols like gRPC.

• YANG Data Models: Structure the telemetry data, ensuring the twin has a standardized, machine-readable view of every device.
• Time-Series Databases: The backend storage required to ingest and query this high-resolution data for historical analysis.

The standardized framework that hosts the digital twin for a disaggregated radio access network. The SMO integrates the Non-Real-Time RIC, which uses the twin for policy-based optimization over >1 second loops.

• R1 Interface: Enables rApps within the SMO to consume data from and interact with the digital twin.
• A1 Interface: Allows the Non-RT RIC to deliver validated, AI-driven policies from the twin to the Near-RT RIC for execution.

The provisioning engine that a digital twin validates. Network configurations are defined as declarative code in a Git repository. The digital twin simulates a proposed change from a pull request, and if validated, a reconciliation loop automatically applies it to the live infrastructure.

• Canary Deployment: The twin can simulate a change on a small subset of the network before full rollout.
• Idempotency: The twin ensures that a configuration applied multiple times always results in the same, stable state.

An advanced AI modeling technique uniquely suited to powering the simulation engine of a digital twin. Unlike traditional models, GNNs can natively learn from the non-Euclidean, graph-based structure of a cellular network, where base stations are nodes and interference patterns are edges.

• Interference Prediction: The GNN-powered twin can accurately simulate complex inter-cell interference for 'what-if' analysis.
• Topology Optimization: It can model the impact of adding or removing a cell site before any physical work begins.