Mavenir > Portfolio > MAVair® > Non-Terrestrial Network (NTN) > NTN AI-By-Design


AI is not optional in space


Mavenir’s AI-native architecture combines an AI Fabric, distributed edge dApps and Network Intelligence as a Service (NIaaS) to make NTN operate autonomously.
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The challenge

Self-Organizing Network (SON)

01
Dynamic Topology
NTN features satellite beams moving at orbital speeds, making static network planning obsolete.

02
Latency and Doppler Effects
GEO round-trip delays of approximately 540 ms disrupt traditional feedback and control loops. Doppler shifts compound the problem.

03
Observability and Data Gaps
Link outages cause significant data loss, undermining analytics and network observability.

04
Need for AI-Native Control
Scale and complexity require AI-driven automation beyond manual SON workflows.

The seven pillars

One AI Fabric Across Every Layer

The AI Fabric runs across RAN, Core, orchestration, lifecycle, business and sustainability, with edge dApps for the latency-critical decisions.

RAN Optimization

AI dynamically allocates satellite resources, adjusts beamforming, and optimizes traffic routes to reduce latency.

Core Network Enhancements

AI-powered network data analytics and seamless TN-NTN integration in the service-based core.

End-to-End Orchestration

AI-driven orchestration across satellite constellations, ground stations, and terrestrial networks in harmony.

Predictive Maintenance

Telemetry analytics identify anomalies and forecast failures, critical when physical satellite maintenance is impossible.

Monetization and Insights

Tools to develop new revenue opportunities and extract business insights from network data.

Sustainability and Energy

Smart resource scheduling and AI power management reduce energy consumption across the NTN ecosystem.

Edge dApp Inference

Distributed AI co-located with the RAN delivers decisions in under 1 ms, where they matter most.

NTN Ai-by-design deployment roadmap

From Observability Today, to Autonomy Tomorrow

2025
Crawl
Observability data collection, KPI monitoring, AI recommendations

2026 H1
Walk
KPI impact prediction via digital twin, controlled automation, policy enforcement

2026 H2
Run
AI Fabric orchestrates multi-domain optimization and real-time edge dApp decisions

2027+
6G Ready
AI-native orchestration across TN+NTN, slicing, reduced capacity devices
Edge AI

dApp

Under 1 ms inference at the network edge.
50% BLER reduction demonstrated.

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Cloud intelligence

NIaaS

Network Intelligence as a Service: model training,
lifecycle management, multi-domain analytics.

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