2021
DOI: 10.48550/arxiv.2106.00845
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Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning

Abstract: Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be deployed to provide wireless connectivity to ground devices in events of increased network demand, points-offailure in existing infrastructure, or disasters. However, it is challenging to conserve the energy of UAVs during prolonged coverage tasks, considering their limited on-board battery capacity. Reinforcement learning-based (RL) approaches have been previously used to improve energy utilization of multiple UAVs, however, a central c… Show more

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