Digital Twin Satellite Networks: A Paradigm for Intelligent, Efficient, and Resilient Operations

2026-08-13

AI Summary

This work addresses the challenges of ensuring efficient and reliable operation in low Earth orbit mega-constellations, which are hindered by highly dynamic topologies, intermittent inter-satellite links, hardware perturbations, and stringent size, weight, and power constraints. To overcome these limitations, the paper proposes a closed-loop Digital Twin Satellite Network (DTSN) architecture that uniquely integrates platform-aware predictive mechanisms with an exponential sensor recovery model. The framework synchronizes virtual twins to jointly incorporate real-time telemetry, integrated sensing and communication, predictive intelligence, and resilient control. Implemented via the NASA 42 simulator coupled with a Python-based digital twin bridge, the system achieves attacked node isolation and proactive network reconfiguration within 600 seconds, effectively mitigating orbital drift, hardware failures, and adversarial interference, thereby significantly enhancing service continuity and overall system resilience.

Abstract

Satellite mega-constellations in Low Earth Orbit (LEO) are becoming an important part of next-generation non-terrestrial networks, but their operation remains challenging because of fast network topology variation, intermittent inter-satellite links, hardware disturbances, and strict Size, Weight, and Power (SWaP) constraints. Existing approaches based on Digital Twin (DT), Digital Twin Network (DTN), Software-Defined Networking (SDN), and Open Radio Access Network (O-RAN) provide useful building blocks for intelligent satellite networking, but they do not fully support real-time, predictive, and platform-aware network operation. In this paper, we propose a Digital Twin Satellite Network (DTSN) framework as a closed-loop architecture for reliable and intelligent management of LEO satellite constellations. The proposed framework connects the physical satellite network with a synchronized virtual twin and combines real-time telemetry, Integrated Sensing and Communication (ISAC), predictive intelligence, and resilience-oriented control. To validate the concept, we develop a constellation-scale cross-domain co-simulation using the NASA 42 spacecraft simulator and a Python-based DT bridge for a LEO constellation. The DT continuously ingests physical telemetry to manage a multi-domain threat environment, encompassing kinematic drift, hardware failures, and adversarial jamming over a 600-second flight window. By leveraging a predictive lookahead mechanism and an exponential sensor recovery model, the framework successfully isolates compromised nodes and triggers proactive network reconfiguration, thereby ensuring uninterrupted service and dynamic network resilience. These results show the potential of DTSN to support predictive and resilience-oriented satellite network operations.

Problem

• LEO satellite constellations
• network topology variation
• inter-satellite links
• SWaP constraints
• real-time predictive operation

Innovation

• Digital Twin Satellite Network
• Predictive Intelligence
• Resilience-Oriented Control
• Integrated Sensing and Communication
• LEO Constellation