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

Isaac Sim, Young Ghyu Sun, Donggu Lee, Soo Hyun Kim, Jiyoung Lee, Jae-Hyun Kim, Yoan Shin and Jin Young Kim
Additional contact information
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

Energies, 2020, vol. 13, issue 23, 1-12

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.

Keywords: successive interference cancellation (SIC); nonorthogonal multiple access (NOMA); imperfect SIC; deep learning; convolutional neural network (CNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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