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Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
1School of Electrical Engineering, Korea University, Seoul 02841, Korea.
Sensors (Basel, Switzerland)
|December 10, 2021
Summary
An adaptable aerial access network (AAN) using unmanned aerial vehicles (UAVs) and federated reinforcement learning (FRL) enhances satellite internet in crowded areas. This system provides significantly more resources and lower latency than satellite-only networks.
Area of Science:
- Network Engineering
- Artificial Intelligence
- Aerospace Communications
Background:
- Satellite internet providers face challenges in densely populated areas due to limited capacity.
- Advances in rocket technology enable new players like SpaceX and Amazon in the satellite internet market.
Purpose of the Study:
- To propose an adaptable aerial access network (AAN) to offload traffic in densely populated areas.
- To leverage federated reinforcement learning (FRL) enabled unmanned aerial vehicles (UAVs) for improved internet service.
Main Methods:
- Developed a system combining low-Earth orbit (LEO) satellites with FRL-enabled UAVs.
- Implemented a UAV-aided AAN simulator for system evaluation.
- Utilized FRL for continuous learning and adaptation in diverse environments.
Main Results:
- The FRL-enabled UAV-aided AAN efficiently serves densely populated areas.
- The proposed AAN system offers 3.25 times more communication resources compared to satellite-only systems.
- Achieved 5.1% lower latency with the AAN system than with satellite-only networks.
Conclusions:
- The FRL-enabled UAV-aided AAN effectively improves network performance in areas with high internet demand.
- UAVs in the AAN require lower computational resources than centralized systems.
- The system demonstrates enhanced efficiency and capacity for future internet delivery.
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