Learning-Driven Decision Mechanisms in Physical Layer: Facts, Challenges, and Remedies
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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.
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