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

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Cetin S. G., Goztepe C., Kurt G. K., Yanikomeroglu H.

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, vol.3, pp.1280-1294, 2022 (ESCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 3
  • Publication Date: 2022
  • Doi Number: 10.1109/ojcoms.2022.3195434
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.1280-1294
  • Keywords: Synchronization, Radio frequency, Machine learning algorithms, Physical layer, Receivers, Correlation, Channel estimation, Cybertwin, decision mechanisms, learning-driven solutions, machine learning, physical layer, real-world impairments, FREQUENCY OFFSET, PERFORMANCE ANALYSIS, WIRELESS NETWORKS, SECURITY THREATS, TIMING RECOVERY, NEURAL-NETWORKS, 5G NETWORKS, COMMUNICATION, CHANNEL, OFDM
  • Istanbul Technical University Affiliated: Yes


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.