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This paper considers an aerial base station (aerial-BS) assisted terrestrialnetwork where user mobility is taken into account. User movement changes thenetwork dynamically which may result in performance loss. To avoid this loss,guarantee a minimum quality-of-service (QoS) and possibly increase the QoS, weadd an aerial-BS to the network. For a fair comparison between the conventionalterrestrial network and the aerial-BS assisted one, we keep the total number ofBSs identical in both networks. Obtaining the best performance in such networkshighly depends on the optimal placement of the aerial-BS. To this end, analgorithm which can rely on general and realistic assumptions and can decidewhere to go based on the past experiences is required. The proposed approachfor this goal is based on a discounted reward reinforcement learning which isknown as Q-learning. Simulation results show this method provides an effectiveplacement strategy which increases the QoS of wireless networks when it isneeded and promises to find the optimum position of the aerial-BS in discreteenvironments.

Efficient 3D Aerial Base Station Placement Considering Users Mobility by Reinforcement Learning