• Machine Learning
• Neural Netkorks
• OFDM receiver
• Wireless communications

Vertical markets and industries are paving the way for a large diversity of heterogeneous services, use cases, and applications in future 5G networks. It is currently common understanding that, to satisfy all those needs, a flexible, adaptable, and programmable network architecture is required. In this context, operators need the ability to automate their architecture configuration and monitoring processes to reduce their OPerational EXpenditure (OPEX), and more importantly to ensure that the quality-of-service and quality-of-experience requirements of the offered services are not violated. The use of Artificial Intelligence (AI) techniques is emerging as a promising solution to achieve these goals and to replace complex and expensive human-dependent decision-making processes. Amongst the many algorithms of the AI family and of its branch Machine Learning, Artificial Neural Networks (ANNs) based schemes are becoming very popular in the context of future 5G networks. The primary objective of this thesis is to develop a fundamental new way to think about communications system design as an end-to-end reconstruction task through ANN that seeks to jointly optimize the transmitter and receiver components in a single process. As a first instance of this design approach, we apply this idea to an Orthogonal Frequency-Division Multiplexing (OFDM) system, and we show how it can be used to implement a receiver architecture that resembles classical receiving schemes without any a priori knowledge about the format of communication standard.