The evolution of LTE towards 5G has started and different research projects and institutions are in the process of verifying new technology components through simulations. Coordination between groups is strongly recommended and, in this sense, a common definition of test cases and simulation models is needed. The scope of this paper is to present a realistic channel model for urban macrocell scenarios. This model is map-based and takes into account the layout of buildings situated in the area under study. A detailed description of the model is given together with a comparison with other widely used channel models. The benchmark includes a measurement campaign in which the proposed model is shown to be much closer to the actual behavior of a cellular system. Particular attention is given to the outdoor component of the model, since it is here where the proposed approach is showing main difference with other previous models.

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 Qlearning technique, a Gradient-based solution and a Greedysearch 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.

The Fifth Generation (5G) of mobile communications has brought a change of paradigm in the way cellular technologies are conceived. 5G has been designed to provide services not only to people, but also to industries and verticals. One of the verticals that will benefit most from the 5G is the Industry 4.0. Channel modeling for this vertical is receiving significant attention, mainly due to the increased complexity that comes with multipath fading scenarios. In order to overcome this problem, the Third Generation Partnership Project (3GPP) defined a new stochastic channel model as part of called Indoor Factory (InF). This paper describes the implementation process of this channel model as an extension tool for one of the most wellknown open-source software simulators, ns-3. Calibration results have been obtained and compared with other 3GPP references. This work permits to use ns-3 as a reliable tool for evaluating new industrial scenarios and use cases.

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