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

arXiv: https://arxiv.org/abs/2102.07258 · HTML: https://mlsanity.com/paper/2102.07258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 122 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 23 of 23)

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## Abstract (arXiv)

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.