Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
Supported by the advances in rocket technology, companies like SpaceX and Amazon competitively have entered the satellite Internet business. These companies said that they could provide Internet service sufficiently to users using their communication resources. However, the Internet service might no...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/23/8111 |
| _version_ | 1797507119173337088 |
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| author | Seungho Yoo Woonghee Lee |
| author_facet | Seungho Yoo Woonghee Lee |
| author_sort | Seungho Yoo |
| collection | DOAJ |
| description | Supported by the advances in rocket technology, companies like SpaceX and Amazon competitively have entered the satellite Internet business. These companies said that they could provide Internet service sufficiently to users using their communication resources. However, the Internet service might not be provided in densely populated areas, as the satellites coverage is broad but its resource capacity is limited. To offload the traffic of the densely populated area, we present an adaptable aerial access network (AAN), composed of low-Earth orbit (LEO) satellites and federated reinforcement learning (FRL)-enabled unmanned aerial vehicles (UAVs). Using the proposed system, UAVs could operate with relatively low computation resources than centralized coverage management systems. Furthermore, by utilizing FRL, the system could continuously learn from various environments and perform better with the longer operation times. Based on our proposed design, we implemented FRL, constructed the UAV-aided AAN simulator, and evaluated the proposed system. Base on the evaluation result, we validated that the FRL enabled UAV-aided AAN could operate efficiently in densely populated areas where the satellites cannot provide sufficient Internet services, which improves network performances. In the evaluations, our proposed AAN system provided about 3.25 times more communication resources and had 5.1% lower latency than the satellite-only AAN. |
| first_indexed | 2024-03-10T04:44:05Z |
| format | Article |
| id | doaj.art-0a363142cddf464a8b246244cb3fa71f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T04:44:05Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0a363142cddf464a8b246244cb3fa71f2023-11-23T03:04:09ZengMDPI AGSensors1424-82202021-12-012123811110.3390/s21238111Federated Reinforcement Learning Based AANs with LEO Satellites and UAVsSeungho Yoo0Woonghee Lee1School of Electrical Engineering, Korea University, Seoul 02841, KoreaDivision of IT Convergence Engineering, Hansung University, Seoul 02876, KoreaSupported by the advances in rocket technology, companies like SpaceX and Amazon competitively have entered the satellite Internet business. These companies said that they could provide Internet service sufficiently to users using their communication resources. However, the Internet service might not be provided in densely populated areas, as the satellites coverage is broad but its resource capacity is limited. To offload the traffic of the densely populated area, we present an adaptable aerial access network (AAN), composed of low-Earth orbit (LEO) satellites and federated reinforcement learning (FRL)-enabled unmanned aerial vehicles (UAVs). Using the proposed system, UAVs could operate with relatively low computation resources than centralized coverage management systems. Furthermore, by utilizing FRL, the system could continuously learn from various environments and perform better with the longer operation times. Based on our proposed design, we implemented FRL, constructed the UAV-aided AAN simulator, and evaluated the proposed system. Base on the evaluation result, we validated that the FRL enabled UAV-aided AAN could operate efficiently in densely populated areas where the satellites cannot provide sufficient Internet services, which improves network performances. In the evaluations, our proposed AAN system provided about 3.25 times more communication resources and had 5.1% lower latency than the satellite-only AAN.https://www.mdpi.com/1424-8220/21/23/8111aerial access networkfederated reinforcement learninglow-Earth orbit satellitespseudo-satellitesnon-terrestrial network |
| spellingShingle | Seungho Yoo Woonghee Lee Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs Sensors aerial access network federated reinforcement learning low-Earth orbit satellites pseudo-satellites non-terrestrial network |
| title | Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs |
| title_full | Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs |
| title_fullStr | Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs |
| title_full_unstemmed | Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs |
| title_short | Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs |
| title_sort | federated reinforcement learning based aans with leo satellites and uavs |
| topic | aerial access network federated reinforcement learning low-Earth orbit satellites pseudo-satellites non-terrestrial network |
| url | https://www.mdpi.com/1424-8220/21/23/8111 |
| work_keys_str_mv | AT seunghoyoo federatedreinforcementlearningbasedaanswithleosatellitesanduavs AT woongheelee federatedreinforcementlearningbasedaanswithleosatellitesanduavs |