A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies

TL;DR

This paper reviews physical layer decision mechanisms, highlights their limitations, and advocates for learning-driven, adaptive solutions to enhance robustness and performance in future communication systems.

Contribution

It identifies common assumptions in physical layer decisions, examines learning algorithms as remedies, and demonstrates potential improvements through a real-time SDR case study.

Findings

Learning algorithms can improve decision robustness.

Real-time SDR experiments show performance gains.

Cyber-physical frameworks support future adaptive solutions.

Abstract

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.