Digital Modulation Classification Based On Chicken Swarm Optimization and J48 Algorithm

Automatic Modulation Recognition (AMR) has a significant impact in the military as well as civil applications. Recognizing the modulation of the received signal has been considered as an intermediate step between the detection and demodulation of the signal. Which is why, in many military and commun...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1879; H. 2; S. 22093 - 22099
Hauptverfasser: Obeas, Zainab Kadhm, Alwaisi, Shaimaa Safaa Ahmed, Abd, Nadie Kadom
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.05.2021
Schlagworte:
Algorithms
Automatic modulation recognition
Chicken Swarm Optimization Algorithm
Classifiers
Communications systems
Demodulation
Feature extraction
Hybrid System
Hybrid systems
J48 Algorithm
Military applications
Military communications
Modules
Optimization
Physics
Signal classification
ISSN:1742-6588, 1742-6596
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Zusammenfassung:Automatic Modulation Recognition (AMR) has a significant impact in the military as well as civil applications. Recognizing the modulation of the received signal has been considered as an intermediate step between the detection and demodulation of the signal. Which is why, in many military and communication systems, the AMR is considered as part of the system. Presently, due to increasing digital modulations in military and civil applications. Digital modulation recognition is especially important. Usually for the AMR a small number of the received signal features are obtained and utilized. The choice of the suitable feature plays an important part in the increase of AMR efficiency. The presented paper indicates hybrid intelligent system for the recognitions of digital signal types, consisting of 3 major modules: classifier module, feature extraction module and J48 Classifier that was used for the first time in our research in the field of classification of modulated signals and optimization module by Chicken Swarm Optimization (CSO). To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features Chicken Swarm Optimization. The results of simulation confirm the high accuracy of recognition that is related to the suggested system even at low SNR.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1879/2/022093