Comparison of Optimization Methods for Aerial Base Station Placement with Users Mobility

Aerial base stations have been recently considered in the deployment of wireless networks. Finding the optimal position for one or multiple aerial base stations is a complex problem tackled by several works. However, just a few works consider the mobility of the users which makes necessary an online optimization to follow the changes in the scenario where the optimization is performed. This paper deals with the online optimization of an aerial base station placement considering different types of users mobility and three algorithms: a Q-learning technique, a Gradient-based solution and a Greedy-search solution. Our objective is to minimize in an urban environment the path loss of the user at street level with the highest path loss. Simulation results show that the performance of the three methods is similar when a high number of users move randomly and uniformly around the scenario under test. Nevertheless, in some situations when the number of users is reduced or when the users move together in a similar direction, both Gradient and Greedy algorithms present a significantly better performance than the Q-learning method.