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Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Author

Listed:
  • Ahmed Alkhayyat
  • Firas Abedi
  • Ashish Bagwari
  • Pooja Joshi
  • Haider Mahmood Jawad
  • Sarmad Nozad Mahmood
  • Yousif K Yousif

Abstract

Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.

Suggested Citation

  • Ahmed Alkhayyat & Firas Abedi & Ashish Bagwari & Pooja Joshi & Haider Mahmood Jawad & Sarmad Nozad Mahmood & Yousif K Yousif, 2022. "Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review," International Journal of Distributed Sensor Networks, , vol. 18(7), pages 15501329221, July.
    • Handle: RePEc:sae:intdis:v:18:y:2022:i:7:p:15501329221113508
    DOI: 10.1177/15501329221113508
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