Objective
INDISPENSATBLE aims to develop a multi-purpose digital twin to optimize and manage the integration of NTN networks into advanced 5G through hybrid links combining radio frequency (RF) and optical interfaces. This digital twin will enable accurate simulation and characterization of the links between ground stations and satellites in various orbits, optimizing connectivity, resource allocation, and communication efficiency, in-line with advanced 5G and 6G standards.
Project Description
Modular design for an advanced system
The system architecture is based on a modular design involving three main components:
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The first module is a 3D visualization module, which represents an interactive globe showing the satellites in orbit, satellite-terminal links, inter-satellite links, and the results generated by the system, providing a detailed and dynamic view of the space network.
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The second module is in charge of advanced simulations, data processing, and complex calculations, as well as managing the communication with the data resources coming from the real object.
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Finally, a module focused on AI services: Optimization, Links Management, Predictive Analysis, Data Analysis, and Decision-Making.
Applications
Advanced solutions for non-terrestrial satellite networks
ADVANCED MONITORING AND MANAGEMENT OF CONSTELLATIONS
The digital twin enables real-time monitoring of the satellite constellation, optimizing service quality through advanced management of hybrid links.
DESIGN OF GLOBAL COVERAGE SATELLITE SYSTEMS
INDISPENSATBLE enables the design and simulation of systems integrating satellite and terrestrial networks, creating global coverage for users on land, sea, and air.
AI-BASED OPTIMIZATION OF SATELLITE COMMUNICATIONS
The implementation of AI in the digital twin facilitates real-time decision-making on transmission strategies. AI selects the optimal routes and configurations to maximize transmission capacity.
Digital Twin
Hybrid Links
AI Management
Monitoring
Services
Digital Twin for LEO Constellations
The implemented DT allows simulating the behaviour of a LEO satellite constellation, including orbital dynamics, integration of synthetic data, and calculation of link budgets between satellites. Even though this model is not connected to a real satellite constellation, it integrates synthetic data that mimics real inputs, providing a highly realistic environment for testing and simulations. This DT offers multiple advantages, such as optimizing resources, performing risk-free tests, anticipating failures with predictive maintenance, and reducing operational risks. In addition, it leaves open the possibility of integrating real data in the future, enabling a direct connection with physical systems for even more accurate simulation.
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Hybrid Optical and RF Communication
The system implements hybrid links that combine optical and radio frequency communications, leveraging the strengths of both technologies. Optical links provide high data transfer rates and low latency, while radio frequency links ensure greater robustness and reliability under adverse conditions, such as atmospheric interferences or meteorological phenomena. This combination enables significant optimization of communication performance within the constellation, enhancing both reliability and link capacity. The dynamic management of these hybrid links is carried out by AI algorithms, which continuously analyse operational conditions in real-time. These algorithms dynamically adjust link parameters to adapt to changing scenarios, ensuring optimal performance, efficient resource allocation, and robust connectivity throughout the network.
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AI for Link Optimization and Management
Artificial Intelligence plays a key role in this system, providing advanced capabilities to calculate key metrics, make accurate predictions, and process data in real-time. Thanks to these functionalities, it is possible to optimize satellite constellation configurations and their operational parameters according to defined objectives, user needs, and weather conditions, both present and future. Among the main contributions of AI are the improvement of simulation performance through advanced Machine Learning algorithms, the anticipation of future system behaviour through data-driven predictions, and the automation of decision-making to select optimal configurations. In addition, AI facilitates the efficient analysis of data obtained during simulations and enables the generation of realistic synthetic data, useful for system testing and validation.
Active Monitoring and Predictive Analysis
Thanks to the digital twin and Artificial Intelligence, the system enables continuous and detailed monitoring of the satellite network status, detecting anomalies in real-time and anticipating potential failures through predictive maintenance algorithms. AI dynamically analyzes operational conditions, automatically adjusting system parameters to ensure optimal and stable communication. This intelligent combination not only improves service quality and significantly reduces operational costs but also allows for safe simulations, optimized resource usage, and network adaptation to changing scenarios and adverse weather conditions.
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The research conducted under the INDISPENSATBLE R&D Project (TSI-064200-2023-0013) has been co-funded by the Ministry of Economic Affairs and Digital Transformation and by the European Union’s NextGenerationEU funds as part of the “Plan de Recuperación, Transformación y Resiliencia” and the “Mecanismo de Recuperación y Resiliencia”.
