A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9845678/ |
| _version_ | 1798037897370140672 |
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| author | Selen Gecgel Cetin Caner Goztepe Gunes Karabulut Kurt Halim Yanikomeroglu |
| author_facet | Selen Gecgel Cetin Caner Goztepe Gunes Karabulut Kurt Halim Yanikomeroglu |
| author_sort | Selen Gecgel Cetin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:32:50Z |
| format | Article |
| id | doaj.art-3d2464958a7d4d5999ac465a1f320ba3 |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-04-11T21:32:50Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-3d2464958a7d4d5999ac465a1f320ba32022-12-22T04:01:53ZengIEEEIEEE Open Journal of the Communications Society2644-125X2022-01-0131280129410.1109/OJCOMS.2022.31954349845678A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and RemediesSelen Gecgel Cetin0https://orcid.org/0000-0002-4744-4691Caner Goztepe1https://orcid.org/0000-0002-9388-5914Gunes Karabulut Kurt2https://orcid.org/0000-0001-7188-2619Halim Yanikomeroglu3https://orcid.org/0000-0003-4776-9354Department of Electronic and Communications Engineering, Istanbul Technical University, Istanbul, TurkeyDepartment of Electronic and Communications Engineering, Istanbul Technical University, Istanbul, TurkeyDepartment of Electrical Engineering, Poly-Grames Research Center, Polytechnique Montreal, Montreal, CanadaDepartment of Systems and Computer Engineering, Carleton University, Ottawa, CanadaCommunications 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.https://ieeexplore.ieee.org/document/9845678/Cybertwindecision mechanismslearning-driven solutionsmachine learningphysical layerreal-world impairments |
| spellingShingle | Selen Gecgel Cetin Caner Goztepe Gunes Karabulut Kurt Halim Yanikomeroglu A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies IEEE Open Journal of the Communications Society Cybertwin decision mechanisms learning-driven solutions machine learning physical layer real-world impairments |
| title | A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies |
| title_full | A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies |
| title_fullStr | A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies |
| title_full_unstemmed | A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies |
| title_short | A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies |
| title_sort | glimpse of physical layer decision mechanisms facts challenges and remedies |
| topic | Cybertwin decision mechanisms learning-driven solutions machine learning physical layer real-world impairments |
| url | https://ieeexplore.ieee.org/document/9845678/ |
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