Open networks become difficult to operate if every component has to be configured independently. Automation is therefore a core part of Open6Ge. Through SMO, RIC and OTAF - Open Telecom Automation Framework, the architecture supports lifecycle and policy management across RAN, Core and cloud infrastructure.

This allows network intelligence to operate across multiple timescales: Non-Real-Time to rApps, Near-Real-Time to xApps, and Real-Time / RAN-local to dApps. Together they create a hierarchical automation model that can progressively move telecom networks toward autonomous operation.

• Network Slice Lifecycle
• SMO & RIC Integration
• AI agents
• O1 / A1 / E2 Interfaces
• Policy & Intent Management
• RAN & Core Configuration
• Closed-Loop Automation
• rApp & xApp Integration
• Network Lifecycle Management

As open systems become increasingly modular and multi-vendor, it becomes necessary to prove that components actually work together. That is where CNTC fits into Open6Ge: it converts telecom and cloud requirements into automated tests, repeatable execution, measurable evidence, graded verdicts and auditable results.

This creates an important progression: Build → Integrate → Experiment → Validate → Certify. Rather than simply demonstrating that a technology works once, the goal is to make results repeatable and reproducible.

• 3GPP Standards-Based Testing
• Automated Conformance Testing
• UPF / 5G User Plane
• 5G Core Control Plane
• RAN Certification
• SCAS Security Testing
• Automated Certificates
• Reproducible Testing
• End-to-End O-RAN Certification

Open6Ge brings AI directly into the RAN scheduler, enabling per-UE intelligence, predictive link adaptation and dynamic scheduling based on real-time network conditions.

Building on TOSSI's OCUDU AI-RAN framework, the stack supports ML-based MCS selection, BSR prediction and CSI prediction, while extending intelligent scheduling towards different network slices with dynamically changing performance requirements and priorities. The approach combines offline training, lightweight in-RAN inference, live model updates and safe fallback to conventional scheduling.

Instead of relying entirely on static scheduling rules, future RAN systems can combine real-time network information with learned models while retaining safe fallback mechanisms. This creates a practical evolution path toward increasingly AI-native radio networks.

• ML-Based Link Adaptation
• Predictive CSI
• Intelligent Scheduling
• Dynamic Slice-Aware Scheduling
• eMBB / URLLC / mMTC Optimization
• Real-Time RAN Inference
• Traffic-Aware Scheduling
• Closed-Loop Optimization
• Online Model Updates

Another major part of the evolution toward 6G is the convergence of terrestrial and non-terrestrial networks. Open6Ge includes an Open NTN Testbed that allows satellite-enabled connectivity to be explored using an end-to-end software environment.

The advantage of a software-based approach is that researchers can evaluate NTN behaviour without always requiring access to dedicated satellite infrastructure. This allows terrestrial 5G systems to gradually evolve toward integrated terrestrial + satellite connectivity.

• 3GPP Release 17 NTN
• GEO & LEO Scenarios
• Satellite Channel Modelling
• Dynamic Propagation & Doppler
• NTN Timing & Synchronization
• Ephemeris & SIB19
• NTN Mobility & Handover
• End-to-End 5G SA Testing
• Software-Based & Reproducible
• Terrestrial-Satellite Integration

Open6Ge extends AI agents beyond server operations into the telecom network stack, giving agents a verifiable identity and controlled authority to interact with RAN, Core, SMO, applications and infrastructure.

The approach uses cryptographic identity, proof-of-possession, policy-based authorization, delegated permissions and revocation so that every agent action can be authenticated and evaluated before it reaches a network function.

• Verifiable AI Agent Identity
• Cryptographic Authentication
• Proof-of-Possession
• Parameter-Level Policies
• Real-Time Revocation
• Policy-Based Access Control
• Secure RAN & Core Operations
• Agent-to-Agent Trust
• AI-Native Network Automation

6G is expected to extend wireless networks beyond communication. With Integrated Sensing and Communication (ISAC), the same radio infrastructure used to transmit information can also be used to understand the physical environment. The Open6Ge ISAC environment explores this using 5G NR signals such as CSI-RS to extract sensing information.

This means the mobile network can gradually evolve from simply connecting devices to becoming a distributed sensing infrastructure. Applications could eventually span industrial environments, mobility, robotics, smart cities, public safety and environmental awareness.

• 5G NR CSI-RS Sensing
• Pseudo-Monostatic Sensing
• Range & Doppler Estimation
• Live Range-Doppler Mapping
• LOS & NLOS Detection
• Monostatic, Bistatic & Multistatic Sensing
• O-RAN Radio Integration
• ISAC Node Antenna Design
• Open & Reproducible Testbed

RAN, Core, IMS, transport, orchestration and cloud infrastructure must work together across open interfaces. The Open6Ge architecture therefore includes integration capabilities across both the RAN and Core.

On the RAN side, technologies such as xFAPI can bridge differences between independently developed L1 and L2 implementations, enabling combinations across open and accelerated RAN platforms.

On the Core side, cloud-native network functions, programmable user planes and open APIs provide a foundation for rapid experimentation. Additional interoperability components such as SETU and HEXAeBPF address integration across IMS, multi-core environments, control-plane/user-plane composition and programmable packet processing.

• L1-L2 Interoperability
• Multi-Vendor RAN Integration
• OAI ↔ OCUDU Integration
• NVIDIA Aerial ↔ OCUDU Integration
• Core-IMS Interworking
• User- & Control-Plane Integration
• Cloud-Native Network Integration
• End-to-End Validation

6G infrastructure being designed today may remain operational for decades. Security therefore needs to consider not only today's threats, but also the long-term impact of quantum computing. Open6Ge integrates post-quantum security across the telecom architecture.

An important principle here is crypto agility. Networks should not be tied permanently to one algorithm. They need the ability to discover deployed cryptography, select policies, migrate between classical, hybrid and post-quantum profiles, manage certificates and keys, and prevent cryptographic downgrade. That capability will be critical for any long-lived 6G infrastructure.

• ML-KEM, ML-DSA, SLH-DSA
• classical/PQ hybrid profiles
• PQC and hybrid X.509 certificates
• PQC-enabled PKI
• TLS 1.3 / mTLS, IPsec / IKEv2
• 5GC SBI protection
• N2/N3 security
• N32/SEPP security
• crypto inventory and discovery
• policy-driven crypto agility

The evolution toward 6G is not only about software. Future telecom infrastructure will also require increasingly specialized compute architectures. Open6Ge therefore explores RISC-V as an open and customizable computing foundation for telecom.

The combination of open software with open compute architectures could allow the telecom community to experiment with new accelerators without being permanently tied to proprietary processor architectures.

• PHY/baseband processing
• MAC/L2
• DU processing
• packet processing
• UPF acceleration
• edge computing
• AI/ML acceleration
• vector processing
• DSP functions
• custom instructions
• hardware/software co-design

Creating digital representations of network and radio environments to evaluate configurations, algorithms and new technologies before deployment.

The Digital Twin platform simulates the whole stack - radio, propagation, orbits, and the data that trains the models. Site-specific propagation comes from 3D scenes through Sionna RT ray tracing, NTN scenarios cover LEO and GEO orbits, and multi-UE RF simulation runs many nr-UEs on one gNB over a simulated radio with no RF hardware needed.

Labelled per-UE, per-slot datasets are captured straight from the twin, so models can be trained on twin data, replayed on held-out scenarios, then deployed with a safe fallback.

• Multi-UE RF Simulation
• Sionna RT Ray Tracing
• 3D Scenes & Per-Path CIR
• NTN Scenarios (LEO / GEO)
• Per-UE Datasets
• PHY & MAC KPIs
• Train → Validate → Deploy
• Repeatable Scenarios
• Testbed Parity