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This paper presents a project-based learning experience to teach mobile communications courses, carried out at Alcalá University, Madrid, Spain. The experience consists of a competition among teams formed by students, who act as consultant companies working for imaginary operators. The objective of the teams is to obtain the best network design and the most economical budget for an imaginary operator in the city of Alcalá de Henares. The proposed learning scheme uses existing concepts of different fields, such as the project-based or competitive games methodologies, and applies them to the teaching of telecommunications subjects. Details of the practical application of the methodology and results obtained at Alcalá University are discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper describes an educational simulation tool, GSMSIM, which has been developed to teach GSM mobile communications in laboratory lectures. The tool has been programmed in OMNET++, a flexible environment which allows its extension to different aspects of GSM technology, such as the simulation of calls, traffic, handoffs, etc. This tool has been used in the courses 'Communication Systems' and 'Telecommunication Networks I', at the University of Alcalá, Madrid, Spain. We report some of the results obtained in three different scenarios simulating a small, medium and large city, with different characteristics of network coverage. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]