Deep Learning Based Channel Estimation and Artificial Noise Aided Security for NOMA-QSM System
In order to avoid eavesdropping with no additional hardware, a secure non-orthogonal multiple access based quadrature spatial modulation (NOMA-QSM) scheme is proposed. Artificial noise (AN) is added to the information-carrying signal at the transmitter, causing interference at the eavesdropper while...
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Published in | SN computer science Vol. 6; no. 3; p. 244 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Singapore
Springer Nature Singapore
01.03.2025
Springer Nature B.V |
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ISSN | 2661-8907 2662-995X 2661-8907 |
DOI | 10.1007/s42979-025-03793-w |
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Abstract | In order to avoid eavesdropping with no additional hardware, a secure non-orthogonal multiple access based quadrature spatial modulation (NOMA-QSM) scheme is proposed. Artificial noise (AN) is added to the information-carrying signal at the transmitter, causing interference at the eavesdropper while not affecting the legitimate users. A deep learning (DL) based channel estimation is also proposed with the rectified linear unit. Simulation results show that due to the addition of AN, the sum secrecy rate of the system increases to 1.6 bits/s/Hz at a 40 dB signal-to-noise ratio (SNR). The sum secrecy rate of the system increases as the number of transmitting antennas (TAs) increases. The proposed DL-based channel estimation scheme provides comparable performance as that of the minimum mean square estimator at low and high SNR values. At 20 dB SNR, the mean square estimation error for the proposed scheme is approximately -17 dB when two TAs are used. This study also analyzes the effect of imperfect channel state information and imperfect successive interference cancellation on the secrecy rate of users. |
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AbstractList | In order to avoid eavesdropping with no additional hardware, a secure non-orthogonal multiple access based quadrature spatial modulation (NOMA-QSM) scheme is proposed. Artificial noise (AN) is added to the information-carrying signal at the transmitter, causing interference at the eavesdropper while not affecting the legitimate users. A deep learning (DL) based channel estimation is also proposed with the rectified linear unit. Simulation results show that due to the addition of AN, the sum secrecy rate of the system increases to 1.6 bits/s/Hz at a 40 dB signal-to-noise ratio (SNR). The sum secrecy rate of the system increases as the number of transmitting antennas (TAs) increases. The proposed DL-based channel estimation scheme provides comparable performance as that of the minimum mean square estimator at low and high SNR values. At 20 dB SNR, the mean square estimation error for the proposed scheme is approximately -17 dB when two TAs are used. This study also analyzes the effect of imperfect channel state information and imperfect successive interference cancellation on the secrecy rate of users. |
ArticleNumber | 244 |
Author | Singh, Shekhar Pratap Pradhan, Pyari Mohan |
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Snippet | In order to avoid eavesdropping with no additional hardware, a secure non-orthogonal multiple access based quadrature spatial modulation (NOMA-QSM) scheme is... |
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SubjectTerms | Antennas Codes Communication Computer Imaging Computer Science Computer Systems Organization and Communication Networks Cyber-Physical System and Artificial Intelligence: Trends and Challenges Data Structures and Information Theory Deep learning Design Eavesdropping Efficiency Information Systems and Communication Service Noise levels Nonorthogonal multiple access Original Research Pattern Recognition and Graphics Quadratures Receivers & amplifiers Signal processing Signal to noise ratio Simulation Software Engineering/Programming and Operating Systems Sums Transmitters Vision Wireless networks |
Title | Deep Learning Based Channel Estimation and Artificial Noise Aided Security for NOMA-QSM System |
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