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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 performanceof millimeter wave (mmW) networks. In particular, the UAV-IR is used tointelligently reflect mmW beamforming signals from a base station towards amobile 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 thepropagation environment, such that the location and reflection coefficient ofthe UAV-IR can be optimized to maximize the downlink transmission capacity.Simulation results show a significant advantage for using a UAV-IR over astatic IR, in terms of the average data rate and the achievable downlink LOSprobability. The results also show that the RL-based deployment of the UAV-IRfurther improves the network performance, relative to a scheme withoutlearning.

Reflections in the Sky: Millimeter Wave Communication with UAV-Carried Intelligent Reflectors