Journal
2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/globecom38437.2019.9013626
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Funding
- U.S. National Science Foundation [CNS-1814477]
- Academy of Finland [294128]
- 6Genesis Flagship [319759]
- Kvantum Institute Strategic Project (SAFARI)
- Academy of Finland thorough the MISSION Project [319759]
- Academy of Finland (AKA) [294128, 294128] Funding Source: Academy of Finland (AKA)
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In this paper, a novel approach that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance the performance of millimeter wave (mmW) networks. In particular, the UAV-IR is used to intelligently reflect mmW beamforming signals from a base station towards a mobile outdoor user, while harvesting energy from mmW signals to power the IR. To maintain a line-of-sight (LOS) channel, a reinforcement learning (RL) approach, based on Q-learning and neural networks, is proposed to model the propagation environment, such that the location and reflection coefficient of the UAV-IR can be optimized to maximize the downlink transmission capacity. Simulation results show a significant advantage for using a UAV-IR over a static IR, in terms of the average data rate and the achievable downlink LOS probability. The results also show that the RL-based deployment of the UAV-IR further improves the network performance, relative to a scheme without learning.
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