Open AccessSurvey on cognitive anti‐jamming communications
Author(s) -
Aref Mohamed A.,
Jayaweera Sudharman K.,
Yepez Esteban
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2020.0024
Subject(s) - jamming , computer science , context (archaeology) , reinforcement learning , cognitive radio , focus (optics) , artificial intelligence , machine learning , telecommunications , wireless , paleontology , physics , thermodynamics , optics , biology
In this study, the authors review various jamming and anti‐jamming strategies in the context of cognitive radios (CRs). The study explores different jamming models and classifies them according to their functionality. Furthermore, a study of jamming detection techniques is provided to enable a CR to identify different jamming signals. Finally, anti‐jamming communications are discussed in detail to encounter different types of jamming attacks. The focus of the study is on advanced anti‐jamming approaches that are based on learning strategies including, for example, game theoretic learning, reinforcement learning and deep learning. They show differences and similarities between these approaches and the conditions under which each of the algorithms can be useful.