  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 a fair comparison between the conventional
terrestrial network and the aerial-BS assisted one, we keep the total number of
BSs identical in both networks. Obtaining the best performance in such networks
highly depends on the optimal placement of the aerial-BS. To this end, an
algorithm which can rely on general and realistic assumptions and can decide
where to go based on the past experiences is required. 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.
