Machine Learning for 5G MIMO Modulation Detection
1
Computer Engineering and Networks Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
2
Institute of Industrial Technology, Korea University, Sejong 30019, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Ahmet Kondoz
Sensors 2021, 21(5), 1556; https://doi.org/10.3390/s21051556
Received: 12 January 2021
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Revised: 31 January 2021
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Accepted: 20 February 2021
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Published: 24 February 2021
(This article belongs to the Special Issue Advanced Physical-Layer Technologies for beyond 5G Wireless Communication Networks)
Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI). At the destination node, we extract the higher-order statistics of the received signals as the discriminating features. After applying the principal component analysis technique, we carry out a comparative study between the random committee and the AdaBoost machine learning techniques (MLTs) at low signal-to-noise ratio. The efficiency metrics, including the true positive rate, false positive rate, precision, recall, F-Measure, and the time taken to build the model, are used for the performance comparison. The simulation results show that the use of the random committee MLT, compared to the AdaBoost MLT, provides gain in terms of both the modulation detection and complexity.
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Keywords:
5G; multi-relay cooperative MIMO systems; modulation detection; random committee machine learning technique
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MDPI and ACS Style
Chikha, H.B.; Almadhor, A.; Khalid, W. Machine Learning for 5G MIMO Modulation Detection. Sensors 2021, 21, 1556. https://doi.org/10.3390/s21051556
AMA Style
Chikha HB, Almadhor A, Khalid W. Machine Learning for 5G MIMO Modulation Detection. Sensors. 2021; 21(5):1556. https://doi.org/10.3390/s21051556
Chicago/Turabian StyleChikha, Haithem B., Ahmad Almadhor, and Waqas Khalid. 2021. "Machine Learning for 5G MIMO Modulation Detection" Sensors 21, no. 5: 1556. https://doi.org/10.3390/s21051556
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