Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?

This work addresses the use of emerging data-driven
techniques based on deep learning and artificial neural networks
in future wireless communication networks. In particular, a key
point that will be made and supported throughout the work is
that data-driven approaches should not replace traditional design
techniques based on mathematical models. On the contrary,
despite being seemingly mutually exclusive, there is much to be
gained by merging data-driven and model-based approaches.
To begin with, a detailed presentation is given for the reasons
why deep learning based on artificial neural networks will be an
indispensable tool for the design and operation of future wireless
communications networks, as well as a description of the recent
technological advances that make deep learning practically viable
for wireless applications. Our vision of how artificial neural
networks should be integrated into the architecture of future
wireless communication networks is presented, explaining the
main areas where deep learning provides a decisive advantage
over traditional approaches.