• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Deep Learning-Based Modulation Detection for NOMA Systems

Vol. 15, No. 2, February 28, 2021
10.3837/tiis.2021.02.015, Download Paper (Free):

Abstract

Since the signal with strong power need be demodulated first for successive interference cancellation (SIC) receiver in non-orthogonal multiple access (NOMA) systems, the base station (BS) need inform the near user terminal (UT), which has allocated higher power, of the far UT’s modulation mode. To avoid unnecessary signaling overhead of control channel, a blind detection algorithm of NOMA signal modulation mode is designed in this paper. Taking the joint constellation density diagrams of NOMA signal as the detection features, the deep residual network is built for classification, so as to detect the modulation mode of NOMA signal. In view of the fact that the joint constellation diagrams are easily polluted by high intensity noise and lose their real distribution pattern, the wavelet denoising method is adopted to improve the quality of constellations. The simulation results represent that the proposed algorithm can achieve satisfactory detection accuracy in NOMA systems. In addition, the factors affecting the recognition performance are also verified and analyzed.


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Cite this article

[IEEE Style]
W. Xie, J. Xiao, J. Yang, J. Wang, X. Peng, C. Yu and P. Zhu, "Deep Learning-Based Modulation Detection for NOMA Systems," KSII Transactions on Internet and Information Systems, vol. 15, no. 2, pp. 658-672, 2021. DOI: 10.3837/tiis.2021.02.015.

[ACM Style]
Wenwu Xie, Jian Xiao, Jinxia Yang, Ji Wang, Xin Peng, Chao Yu, and Peng Zhu. 2021. Deep Learning-Based Modulation Detection for NOMA Systems. KSII Transactions on Internet and Information Systems, 15, 2, (2021), 658-672. DOI: 10.3837/tiis.2021.02.015.