  Communications are realized as a result of successive decisions at the
physical layer, from modulation selection to multi-antenna strategy, and each
decision affects the performance of the communication systems. Future
communication systems must include extensive capabilities as they will
encompass a wide variety of devices and applications. Conventional physical
layer decision mechanisms may not meet these requirements, as they are often
based on impractical and oversimplifying assumptions that result in a trade-off
between complexity and efficiency. By leveraging past experiences,
learning-driven designs are promising solutions to present a resilient decision
mechanism and enable rapid response even under exceptional circumstances. The
corresponding design solutions should evolve following the lines of
learning-driven paradigms that offer more autonomy and robustness. This
evolution must take place by considering the facts of real-world systems and
without restraining assumptions. In this paper, the common assumptions in the
physical layer are presented to highlight their discrepancies with practical
systems. As a solution, learning algorithms are examined by considering the
implementation steps and challenges. Furthermore, these issues are discussed
through a real-time case study using software-defined radio nodes to
demonstrate the potential performance improvement. A cyber-physical framework
is presented to incorporate future remedies.
