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A new generation of completely flexible satellites in terms of mission definition is currently appearing in the space segment as a response to the 5G revolution, resulting in an unprecedented integration of the satellite services with the terrestrial deployments. In this context, the Digital Transparent Processor (DTP) is aimed to constitute the core of these digital payload, providing them with new capabilities unthinkable to achieve with the traditional analogue ones, such as adaptive bandwidth (BW) and power allocation, or flexibility in the connectivity and coverage.
As a consequence, the flexibility offered by these kind of satellites leads to the increased complexity in their management. Then, the optimum reconfiguration of these complex payloads to fulfil the users requirements is no longer an achievable task for a payload engineer due to the real-time constraints and the number of possibilities. To solve this, these satellites require new and intelligent tools capable of optimally allocating the satellite resources, while monitoring the interferences in such a dynamic scenario.
In this point, Artificial Intelligence/Machine Learning (AI/ML) algorithms appear to substitute the operator decisions and manual operations while configuring the payloads. In atria project, these algorithms will compose an AI/ML module in a Payload optimization Control System (AI-PCS). The algorithms are to be trained with both real data sets of former manual operations and synthetic data. Also, the algorithms will receive information from external services to provide them with inputs regarding meteorology, traffic, availability, etc. in order to achieve the optimum decisions.
AI-PCS is to be validated using both a flexible payload emulator and real satellite environments. AI-PCS is aimed to be a generic tool, transparent to the payload, providing the satcom operators with added value and turning it into a cost-effective solution for the future of the ground segment.
As a consequence, the flexibility offered by these kind of satellites leads to the increased complexity in their management. Then, the optimum reconfiguration of these complex payloads to fulfil the users requirements is no longer an achievable task for a payload engineer due to the real-time constraints and the number of possibilities. To solve this, these satellites require new and intelligent tools capable of optimally allocating the satellite resources, while monitoring the interferences in such a dynamic scenario.
In this point, Artificial Intelligence/Machine Learning (AI/ML) algorithms appear to substitute the operator decisions and manual operations while configuring the payloads. In atria project, these algorithms will compose an AI/ML module in a Payload optimization Control System (AI-PCS). The algorithms are to be trained with both real data sets of former manual operations and synthetic data. Also, the algorithms will receive information from external services to provide them with inputs regarding meteorology, traffic, availability, etc. in order to achieve the optimum decisions.
AI-PCS is to be validated using both a flexible payload emulator and real satellite environments. AI-PCS is aimed to be a generic tool, transparent to the payload, providing the satcom operators with added value and turning it into a cost-effective solution for the future of the ground segment.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101004215 |
| Start date: | 01-04-2021 |
| End date: | 31-03-2024 |
| Total budget - Public funding: | 2 999 490,00 Euro - 2 999 490,00 Euro |
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Original description
A new generation of completely flexible satellites in terms of mission definition is currently appearing in the space segment as a response to the 5G revolution, resulting in an unprecedented integration of the satellite services with the terrestrial deployments. In this context, the Digital Transparent Processor (DTP) is aimed to constitute the core of these digital payload, providing them with new capabilities unthinkable to achieve with the traditional analogue ones, such as adaptive bandwidth (BW) and power allocation, or flexibility in the connectivity and coverage.As a consequence, the flexibility offered by these kind of satellites leads to the increased complexity in their management. Then, the optimum reconfiguration of these complex payloads to fulfil the users requirements is no longer an achievable task for a payload engineer due to the real-time constraints and the number of possibilities. To solve this, these satellites require new and intelligent tools capable of optimally allocating the satellite resources, while monitoring the interferences in such a dynamic scenario.
In this point, Artificial Intelligence/Machine Learning (AI/ML) algorithms appear to substitute the operator decisions and manual operations while configuring the payloads. In atria project, these algorithms will compose an AI/ML module in a Payload optimization Control System (AI-PCS). The algorithms are to be trained with both real data sets of former manual operations and synthetic data. Also, the algorithms will receive information from external services to provide them with inputs regarding meteorology, traffic, availability, etc. in order to achieve the optimum decisions.
AI-PCS is to be validated using both a flexible payload emulator and real satellite environments. AI-PCS is aimed to be a generic tool, transparent to the payload, providing the satcom operators with added value and turning it into a cost-effective solution for the future of the ground segment.
Status
SIGNEDCall topic
SPACE-29-TEC-2020Update Date
27-10-2022
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