This work deals with the use of emerging deep learning techniques in future
wireless communication networks. It will be shown that data-driven approaches
should not replace, but rather complement traditional design techniques based
on mathematical models.
Extensive motivation is given for why deep learning based on artificial
neural networks will be an indispensable tool for the design and operation of
future wireless communications networks, and our vision of how artificial
neural networks should be integrated into the architecture of future wireless
communication networks is presented.
A thorough description of deep learning methodologies is provided, starting
with the general machine learning paradigm, followed by a more in-depth
discussion about deep learning and artificial neural networks, covering the
most widely-used artificial neural network architectures and their training
methods. Deep learning will also be connected to other major learning
frameworks such as reinforcement learning and transfer learning.
A thorough survey of the literature on deep learning for wireless
communication networks is provided, followed by a detailed description of
several novel case-studies wherein the use of deep learning proves extremely
useful for network design. For each case-study, it will be shown how the use of
(even approximate) mathematical models can significantly reduce the amount of
live data that needs to be acquired/measured to implement data-driven
approaches. For each application, the merits of the proposed approaches will be
demonstrated by a numerical analysis in which the implementation and training
of the artificial neural network used to solve the problem is discussed.
Finally, concluding remarks describe those that in our opinion are the major
directions for future research in this field.