  The unprecedented requirements of the Internet of Things (IoT) have made
fine-grained optimization of spectrum resources an urgent necessity. Thus,
designing techniques able to extract knowledge from the spectrum in real time
and select the optimal spectrum access strategy accordingly has become more
important than ever. Moreover, 5G and beyond (5GB) networks will require
complex management schemes to deal with problems such as adaptive beam
management and rate selection. Although deep learning (DL) has been successful
in modeling complex phenomena, commercially-available wireless devices are
still very far from actually adopting learning-based techniques to optimize
their spectrum usage. In this paper, we first discuss the need for real-time DL
at the physical layer, and then summarize the current state of the art and
existing limitations. We conclude the paper by discussing an agenda of research
challenges and how DL can be applied to address crucial problems in 5GB
networks.
