Efficient 3D Aerial Base Station Placement Considering Users Mobility by Reinforcement Learning
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This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality of service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For fair comparison between the conventional terrestrial network and the aerial BS assisted one, we keep the total number of BSs similar in both networks. Obtaining the max performance in such networks highly depends on the optimal ultimate placement of the aerial-BS. To this end, we need an algorithm which can rely on more general and realistic assumptions and can decide where to go based on the past experiences. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find the optimum position of the aerial-BS in discrete environments.
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