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Deep Learning Based Successive Interference Cancellation Scheme in Nonorthogonal Multiple Access Downlink Network

Author

Listed:
  • Isaac Sim

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Young Ghyu Sun

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Donggu Lee

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Soo Hyun Kim

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Jiyoung Lee

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Korea)

  • Jae-Hyun Kim

    (Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea)

  • Yoan Shin

    (School of Electronic Engineering, Soongsil University, Seoul 06978, Korea)

  • Jin Young Kim

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Korea)

Abstract

In this paper, a deep learning-based successive interference cancellation (SIC) scheme for use in nonorthogonal multiple access (NOMA) communication systems is investigated. NOMA has become a notable technique in the field of mobile wireless communication because of its capacity to overcome orthogonality, unlike a conventional orthogonal frequency division multiple access (OFDMA) communication system. In NOMA communication systems, SIC is one of the decoding schemes applied at receivers for downlink NOMA transmissions. In this paper, a convolutional neural network (CNN)-based SIC scheme is proposed to improve performance of the single base station and multiuser NOMA scheme. In contrast to existing SIC schemes, the proposed CNN-based SIC scheme can effectively mitigate losses resulting from imperfections of the SIC. The simulation results indicate that the CNN-based SIC method can successfully relieve conventional SIC impairments and achieve good detection performance. Consequently, a CNN-based SIC scheme can be considered as a potential technique for use in NOMA detection schemes.

Suggested Citation

  • Isaac Sim & Young Ghyu Sun & Donggu Lee & Soo Hyun Kim & Jiyoung Lee & Jae-Hyun Kim & Yoan Shin & Jin Young Kim, 2020. "Deep Learning Based Successive Interference Cancellation Scheme in Nonorthogonal Multiple Access Downlink Network," Energies, MDPI, vol. 13(23), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6237-:d:451798
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    Cited by:

    1. Oh-Soon Shin, 2022. "Energy Efficient Design and Control of Non-Orthogonal Multiple Access Systems," Energies, MDPI, vol. 15(3), pages 1-2, January.

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