GenAI-Based Models for NGSO Satellites Interference Detection
Recent advancements in satellite communications have highlighted the challenge of interference detection, especially with the new generation of non-geostationary orbit satellites (NGSOs) that share the same frequency bands as legacy geostationary orbit satellites (GSOs). Despite existing radio regul...
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10570488/ |
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| author | Almoatssimbillah Saifaldawla Flor Ortiz Eva Lagunas Abuzar B. M. Adam Symeon Chatzinotas |
| author_facet | Almoatssimbillah Saifaldawla Flor Ortiz Eva Lagunas Abuzar B. M. Adam Symeon Chatzinotas |
| author_sort | Almoatssimbillah Saifaldawla |
| collection | DOAJ |
| description | Recent advancements in satellite communications have highlighted the challenge of interference detection, especially with the new generation of non-geostationary orbit satellites (NGSOs) that share the same frequency bands as legacy geostationary orbit satellites (GSOs). Despite existing radio regulations during the filing stage, this heightened congestion in the spectrum is likely to lead to instances of interference during real-time operations. This paper addresses the NGSO-to-GSO interference problem by proposing advanced artificial intelligence (AI) models to detect interference events. In particular, we focus on the downlink interference case, where signals from low-Earth orbit satellites (LEOs) potentially impact the signals received at the GSO ground stations (GGSs). In addition to the widely used autoencoder-based models (AEs), we design, develop, and train two generative AI-based models (GenAI), which are a variational autoencoder (VAE) and a transformer-based interference detector (TrID). These models generate samples of the expected GSO signal, whose error with respect to the input signal is used to flag interference. Actual satellite positions, trajectories, and realistic system parameters are used to emulate the interference scenarios and validate the proposed models. Numerical evaluation reveals that the models exhibit higher accuracy for detecting interference in the time-domain signal representations compared to the frequency-domain representations. Furthermore, the results demonstrate that TrID significantly outperforms the other models as well as the traditional energy detector (ED) approach, showing an increase of up to 31.23% in interference detection accuracy, offering an innovative and efficient solution to a pressing challenge in satellite communications. |
| format | Article |
| id | doaj-art-ff1a950031394ebd9d8d328b87f73c67 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-ff1a950031394ebd9d8d328b87f73c672025-08-20T02:59:24ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-01290492410.1109/TMLCN.2024.341893310570488GenAI-Based Models for NGSO Satellites Interference DetectionAlmoatssimbillah Saifaldawla0https://orcid.org/0000-0003-1053-0649Flor Ortiz1https://orcid.org/0000-0002-2280-4689Eva Lagunas2https://orcid.org/0000-0002-9936-7245Abuzar B. M. Adam3https://orcid.org/0000-0002-9231-9734Symeon Chatzinotas4https://orcid.org/0000-0001-5122-0001Interdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, Kirchberg, LuxembourgInterdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, Kirchberg, LuxembourgInterdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, Kirchberg, LuxembourgInterdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, Kirchberg, LuxembourgInterdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, Kirchberg, LuxembourgRecent advancements in satellite communications have highlighted the challenge of interference detection, especially with the new generation of non-geostationary orbit satellites (NGSOs) that share the same frequency bands as legacy geostationary orbit satellites (GSOs). Despite existing radio regulations during the filing stage, this heightened congestion in the spectrum is likely to lead to instances of interference during real-time operations. This paper addresses the NGSO-to-GSO interference problem by proposing advanced artificial intelligence (AI) models to detect interference events. In particular, we focus on the downlink interference case, where signals from low-Earth orbit satellites (LEOs) potentially impact the signals received at the GSO ground stations (GGSs). In addition to the widely used autoencoder-based models (AEs), we design, develop, and train two generative AI-based models (GenAI), which are a variational autoencoder (VAE) and a transformer-based interference detector (TrID). These models generate samples of the expected GSO signal, whose error with respect to the input signal is used to flag interference. Actual satellite positions, trajectories, and realistic system parameters are used to emulate the interference scenarios and validate the proposed models. Numerical evaluation reveals that the models exhibit higher accuracy for detecting interference in the time-domain signal representations compared to the frequency-domain representations. Furthermore, the results demonstrate that TrID significantly outperforms the other models as well as the traditional energy detector (ED) approach, showing an increase of up to 31.23% in interference detection accuracy, offering an innovative and efficient solution to a pressing challenge in satellite communications.https://ieeexplore.ieee.org/document/10570488/Non-geostationary orbit satellites (NGSOs)geostationary orbit satellites (GSOs)interference detectionsatellite communicationgenerative AI (GenAI) |
| spellingShingle | Almoatssimbillah Saifaldawla Flor Ortiz Eva Lagunas Abuzar B. M. Adam Symeon Chatzinotas GenAI-Based Models for NGSO Satellites Interference Detection IEEE Transactions on Machine Learning in Communications and Networking Non-geostationary orbit satellites (NGSOs) geostationary orbit satellites (GSOs) interference detection satellite communication generative AI (GenAI) |
| title | GenAI-Based Models for NGSO Satellites Interference Detection |
| title_full | GenAI-Based Models for NGSO Satellites Interference Detection |
| title_fullStr | GenAI-Based Models for NGSO Satellites Interference Detection |
| title_full_unstemmed | GenAI-Based Models for NGSO Satellites Interference Detection |
| title_short | GenAI-Based Models for NGSO Satellites Interference Detection |
| title_sort | genai based models for ngso satellites interference detection |
| topic | Non-geostationary orbit satellites (NGSOs) geostationary orbit satellites (GSOs) interference detection satellite communication generative AI (GenAI) |
| url | https://ieeexplore.ieee.org/document/10570488/ |
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