Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct Communications
We investigate effective interference management for Low Earth Orbit (LEO) satellite networks that provide downlink services to ground users and share the same frequency spectrum range. Since there are multi-group LEO satellites with different constellation orbits, the ground users will experience t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10063851/ |
| _version_ | 1797870674900942848 |
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| author | Jihyeon Yun Taegun An Haesung Jo Bon-Jun Ku Daesub Oh Changhee Joo |
| author_facet | Jihyeon Yun Taegun An Haesung Jo Bon-Jun Ku Daesub Oh Changhee Joo |
| author_sort | Jihyeon Yun |
| collection | DOAJ |
| description | We investigate effective interference management for Low Earth Orbit (LEO) satellite networks that provide downlink services to ground users and share the same frequency spectrum range. Since there are multi-group LEO satellites with different constellation orbits, the ground users will experience time-varying interference due to the overlapping of main/side lobes of the satellite beams, which becomes even more challenging when the interfering satellites cannot communicate directly. To address the problem, we consider two LEO satellite groups that provide communication service in the same ground area, while competing for communication resources. We develop solutions that maximize the throughput and manage the time-varying interference under a certain level, without explicit message exchanges between the satellite groups. By exploiting statistical learning and deep reinforcement learning techniques, we develop learning-based resource allocation schemes and evaluate their performance through extensive simulations. We show their effectiveness under different reward settings and different interference managements, and demonstrate that a Deep Q-Network (DQN)-based scheme can achieve the close-to-optimal performance. |
| first_indexed | 2024-04-10T00:32:15Z |
| format | Article |
| id | doaj.art-93e6436c736e416ab39204ff77b34d4e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T00:32:15Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-93e6436c736e416ab39204ff77b34d4e2023-03-14T23:00:31ZengIEEEIEEE Access2169-35362023-01-0111241372414810.1109/ACCESS.2023.325387010063851Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct CommunicationsJihyeon Yun0https://orcid.org/0000-0003-3383-2789Taegun An1Haesung Jo2https://orcid.org/0009-0000-6615-1037Bon-Jun Ku3Daesub Oh4https://orcid.org/0000-0003-4048-8031Changhee Joo5https://orcid.org/0000-0003-1690-2298Computer Science and Engineering, Korea University, Seoul, South KoreaComputer Science and Engineering, Korea University, Seoul, South KoreaComputer Science and Engineering, Korea University, Seoul, South KoreaElectronics and Telecommunications Research Institute (ETRI), Daejeon, South KoreaElectronics and Telecommunications Research Institute (ETRI), Daejeon, South KoreaComputer Science and Engineering, Korea University, Seoul, South KoreaWe investigate effective interference management for Low Earth Orbit (LEO) satellite networks that provide downlink services to ground users and share the same frequency spectrum range. Since there are multi-group LEO satellites with different constellation orbits, the ground users will experience time-varying interference due to the overlapping of main/side lobes of the satellite beams, which becomes even more challenging when the interfering satellites cannot communicate directly. To address the problem, we consider two LEO satellite groups that provide communication service in the same ground area, while competing for communication resources. We develop solutions that maximize the throughput and manage the time-varying interference under a certain level, without explicit message exchanges between the satellite groups. By exploiting statistical learning and deep reinforcement learning techniques, we develop learning-based resource allocation schemes and evaluate their performance through extensive simulations. We show their effectiveness under different reward settings and different interference managements, and demonstrate that a Deep Q-Network (DQN)-based scheme can achieve the close-to-optimal performance.https://ieeexplore.ieee.org/document/10063851/LEO satellite networksinterference managementspectrum sharingDeep Q-Network |
| spellingShingle | Jihyeon Yun Taegun An Haesung Jo Bon-Jun Ku Daesub Oh Changhee Joo Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct Communications IEEE Access LEO satellite networks interference management spectrum sharing Deep Q-Network |
| title | Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct Communications |
| title_full | Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct Communications |
| title_fullStr | Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct Communications |
| title_full_unstemmed | Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct Communications |
| title_short | Dynamic Downlink Interference Management in LEO Satellite Networks Without Direct Communications |
| title_sort | dynamic downlink interference management in leo satellite networks without direct communications |
| topic | LEO satellite networks interference management spectrum sharing Deep Q-Network |
| url | https://ieeexplore.ieee.org/document/10063851/ |
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