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

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