Artificial Intelligence Satellite Telecommunication Testbed using Commercial Off-The-Shelf Chipsets
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866916499064619008 |
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| author | Garcés-Socarrás, Luis M. Nik, Amirhossein Ortiz, Flor Vásquez-Peralvo, Juan A. González-Rios, Jorge L. Chehailty, Mouhamad Kuhfuss, Marcele Lagunas, Eva Thoemel, Jan Kumar, Sumit Singh, Vishal Duncan, Juan C. Merlano Malmir, Sahar Varadajulu, Swetha Querol, Jorge Chatzinotas, Symeon |
| author_facet | Garcés-Socarrás, Luis M. Nik, Amirhossein Ortiz, Flor Vásquez-Peralvo, Juan A. González-Rios, Jorge L. Chehailty, Mouhamad Kuhfuss, Marcele Lagunas, Eva Thoemel, Jan Kumar, Sumit Singh, Vishal Duncan, Juan C. Merlano Malmir, Sahar Varadajulu, Swetha Querol, Jorge Chatzinotas, Symeon |
| contents | The Artificial Intelligence Satellite Telecommunications Testbed (AISTT), part of the ESA project SPAICE, is focused on the transformation of the satellite payload by using artificial intelligence (AI) and machine learning (ML) methodologies over available commercial off-the-shelf (COTS) AI-capable chips for onboard processing. The objectives include validating artificial intelligence-driven SATCOM scenarios such as interference detection, spectrum sharing, radio resource management, decoding, and beamforming. The study highlights hardware selection and payload architecture. Preliminary results show that ML models significantly improve signal quality, spectral efficiency, and throughput compared to conventional payload. Moreover, the testbed aims to evaluate the performance and the use of AI-capable COTS chips in onboard SATCOM contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18297 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence Satellite Telecommunication Testbed using Commercial Off-The-Shelf Chipsets Garcés-Socarrás, Luis M. Nik, Amirhossein Ortiz, Flor Vásquez-Peralvo, Juan A. González-Rios, Jorge L. Chehailty, Mouhamad Kuhfuss, Marcele Lagunas, Eva Thoemel, Jan Kumar, Sumit Singh, Vishal Duncan, Juan C. Merlano Malmir, Sahar Varadajulu, Swetha Querol, Jorge Chatzinotas, Symeon Signal Processing The Artificial Intelligence Satellite Telecommunications Testbed (AISTT), part of the ESA project SPAICE, is focused on the transformation of the satellite payload by using artificial intelligence (AI) and machine learning (ML) methodologies over available commercial off-the-shelf (COTS) AI-capable chips for onboard processing. The objectives include validating artificial intelligence-driven SATCOM scenarios such as interference detection, spectrum sharing, radio resource management, decoding, and beamforming. The study highlights hardware selection and payload architecture. Preliminary results show that ML models significantly improve signal quality, spectral efficiency, and throughput compared to conventional payload. Moreover, the testbed aims to evaluate the performance and the use of AI-capable COTS chips in onboard SATCOM contexts. |
| title | Artificial Intelligence Satellite Telecommunication Testbed using Commercial Off-The-Shelf Chipsets |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2405.18297 |