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Article

Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks

1
School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511406, China
2
School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
3
CAS Key Laboratory of Wireless-Optical Communications, University of Science and Technology of China, Hefei 230027, China
4
Department of Engineering, Manchester Metropolitan University, Manchester M15 6BH, UK
5
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1730; https://doi.org/10.3390/s20061730
Received: 22 January 2020 / Revised: 18 March 2020 / Accepted: 18 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Physical Layer Security for Sensor Enabled Heterogeneous Networks)
Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm. View Full-Text
Keywords: physical-layer security; deep learning; imperfect CSI; heterogeneous networks; channel estimation physical-layer security; deep learning; imperfect CSI; heterogeneous networks; channel estimation
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MDPI and ACS Style

Deng, D.; Li, X.; Zhao, M.; Rabie, K.M.; Kharel, R. Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks. Sensors 2020, 20, 1730. https://doi.org/10.3390/s20061730

AMA Style

Deng D, Li X, Zhao M, Rabie KM, Kharel R. Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks. Sensors. 2020; 20(6):1730. https://doi.org/10.3390/s20061730

Chicago/Turabian Style

Deng, Dan, Xingwang Li, Ming Zhao, Khaled M. Rabie, and Rupak Kharel. 2020. "Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks" Sensors 20, no. 6: 1730. https://doi.org/10.3390/s20061730

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