  Traditional communication system design has always been based on the paradigm
of first establishing a mathematical model of the communication channel, then
designing and optimizing the system according to the model. The advent of
modern machine learning techniques, specifically deep neural networks, has
opened up opportunities for data-driven system design and optimization. This
article draws examples from the optimization of reconfigurable intelligent
surface, distributed channel estimation and feedback for multiuser beamforming,
and active sensing for millimeter wave (mmWave) initial alignment to illustrate
that a data-driven design that bypasses explicit channel modelling can often
discover excellent solutions to communication system design and optimization
problems that are otherwise computationally difficult to solve. We show that by
performing an end-to-end training of a deep neural network using a large number
of channel samples, a machine learning based approach can potentially provide
significant system-level improvements as compared to the traditional
model-based approach for solving optimization problems. The key to the
successful applications of machine learning techniques is in choosing the
appropriate neural network architecture to match the underlying problem
structure.
