  Future communication systems must include extensive capabilities as they will
embrace a vast diversity of devices and applications. Conventional physical
layer decision mechanisms may not meet these requirements due to the frequent
use of impracticable and oversimplifying assumptions that lead to a trade-off
between complexity and efficiency. By utilizing past experiences,
learning-driven designs are promising solutions to present a resilient decision
mechanism and provide a quick response even under exceptional circumstances.
The corresponding design solutions should evolve following the learning-driven
paradigms that offer increased autonomy and robustness. This evolution must
take place by considering the facts of real-world systems without restraining
assumptions. This paper introduces the common assumptions in the physical layer
to highlight their discrepancies with practical systems. As a solution,
learning algorithms are examined by considering implementation steps and
challenges. Additionally, these issues are discussed through a real-time case
study that uses software-defined radio nodes, demonstrating the potential
performance improvement. A remedial perspective is presented to guide future
studies.
